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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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33 pages, 16520 KiB  
Article
Enhanced Non-Destructive Testing of Small Wind Turbine Blades Using Infrared Thermography
by Majid Memari, Mohammad Shekaramiz, Mohammad A. S. Masoum and Abdennour C. Seibi
Machines 2025, 13(2), 108; https://doi.org/10.3390/machines13020108 - 29 Jan 2025
Viewed by 960
Abstract
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this [...] Read more.
This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs). Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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29 pages, 4785 KiB  
Review
A Review of the Applications and Challenges of Dielectric Elastomer Actuators in Soft Robotics
by Qinghai Zhang, Wei Yu, Jianghua Zhao, Chuizhou Meng and Shijie Guo
Machines 2025, 13(2), 101; https://doi.org/10.3390/machines13020101 - 27 Jan 2025
Viewed by 2047
Abstract
As an electrically driven artificial muscle, dielectric elastomer actuators (DEAs) are notable for their large deformation, fast response speed, and high energy density, showing significant potential in soft robots. The paper discusses the working principles of DEAs, focusing on their reversible deformation under [...] Read more.
As an electrically driven artificial muscle, dielectric elastomer actuators (DEAs) are notable for their large deformation, fast response speed, and high energy density, showing significant potential in soft robots. The paper discusses the working principles of DEAs, focusing on their reversible deformation under electric fields and performance optimization through material and structural innovations. Key applications include soft grippers, locomotion robots (e.g., multilegged, crawling, swimming, and jumping/flying), humanoid robots, and wearable devices. The challenges associated with DEAs are also examined, including the actuation properties of DE material, material fatigue, viscoelastic effects, and environmental adaptability. Finally, modeling and control strategies to enhance DEA performance are introduced, with a perspective on future technological advancements in the field. Full article
(This article belongs to the Special Issue Dielectric Elastomer Actuators: Theory, Modeling and Application)
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21 pages, 39765 KiB  
Article
Numerical Simulation and Experimental Study of Piston Rebound Energy Storage Characteristics for Nitrogen-Hydraulic Combined Impact Hammer
by Hu Chen, Boqiang Shi and Hui Guo
Machines 2025, 13(2), 97; https://doi.org/10.3390/machines13020097 - 26 Jan 2025
Viewed by 636
Abstract
The objective of this study is to analyze the piston rebound energy storage characteristics of the nitrogen-hydraulic combined impact hammer and to investigate the manner in which the piston rebound energy is converted and utilized. The kinetic equation of the impact hammer system [...] Read more.
The objective of this study is to analyze the piston rebound energy storage characteristics of the nitrogen-hydraulic combined impact hammer and to investigate the manner in which the piston rebound energy is converted and utilized. The kinetic equation of the impact hammer system is established. A numerical calculation model is constructed based on AMEsim, which incorporates the piston, cylinders, reversing valve, accumulator, power source, drill rod, and impacted device. The performance experiment system is designed, the oil pressure experiment and the piston motion experiment are constructed, and the accuracy of the numerical calculation model is verified by comparing the numerical calculation results with the experimental results. This paper investigates the fundamental principles of the piston rebound energy storage and analyzes the relationship between the opening percentage of the reversing valve high-pressure port and the piston rebound energy storage at the outset of the rebound stage. Furthermore, the influence of the length of the piston middle section and the number of high-pressure grooves in the signal chamber on the piston rebound energy storage is investigated. Finally, the experimental comparison allows for an analysis of the influence of the piston rebound energy storage on the performance of the nitrogen-hydraulic combined impact hammer. Full article
(This article belongs to the Section Machine Design and Theory)
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14 pages, 3703 KiB  
Article
Artificial Neural Network-Based Structural Analysis of 3D-Printed Polyethylene Terephthalate Glycol Tensile Specimens
by Athanasios Manavis, Anastasios Tzotzis, Lazaros Firtikiadis and Panagiotis Kyratsis
Machines 2025, 13(2), 86; https://doi.org/10.3390/machines13020086 - 23 Jan 2025
Viewed by 737
Abstract
Materials are a mainstay of both industry and everyday life. The manufacturing and processing of materials is a very important sector as it affects both the mechanical properties and the usage of the final products. In recent years, the increased use of 3D [...] Read more.
Materials are a mainstay of both industry and everyday life. The manufacturing and processing of materials is a very important sector as it affects both the mechanical properties and the usage of the final products. In recent years, the increased use of 3D printing and, by extension, its materials have caused the creation of gaps in terms of strength that require further scientific study. In this study, the influence of various printing parameters on 3D-printed specimens made of polyethylene terephthalate glycol (PETG) polymer was tested. More specifically, three printing parameters were selected—infill, speed, and type—with three different values each (50%, 70%, and 90%), (5 mm/s, 20 mm/s, and 35 mm/s) and (Grid, Rectilinear, and Wiggle). From the combinations of the three parameters and the three values, 27 different specimens were obtained and thus, 27 equivalent experiments were designed. The measurements were evaluated, and the process was modeled with the Artificial Neural Network (ANN) method, revealing a strong and robust prediction model for the tensile test, with the relative error being below 10%. Both infill density and infill pattern were identified as the most influential parameters, with the Wiggle type being the strongest pattern of all. Additionally, it was found that the infill density acts increasingly on the strength, whereas the printing speed acts decreasingly. Full article
(This article belongs to the Section Advanced Manufacturing)
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27 pages, 14486 KiB  
Article
Hollow Direct Air-Cooled Rotor Windings: Conjugate Heat Transfer Analysis
by Avo Reinap, Samuel Estenlund and Conny Högmark
Machines 2025, 13(2), 89; https://doi.org/10.3390/machines13020089 - 23 Jan 2025
Viewed by 641
Abstract
This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and [...] Read more.
This article focuses on the analysis of a direct air-cooled rotor winding of a wound field synchronous machine, the innovation of which lies in the increase in the internal cooling surface, the cooling of the winding compared to the conventional inter-pole cooling, and the development of a CHT evaluation model accordingly. Conjugate heat transfer (CHT) analysis is used to explore the cooling efficacy of a parallel-cooled hollow-conductor winding of a salient-pole rotor and to identify a cooling performance map. The use of high current densities of 15–20 Arms/mm2 in directly cooled windings requires high cooling intensity, which in the case of air cooling results not only in flow velocities above 15 m/s to ensure permissible operating temperatures, but also the need for coolant distribution and heat transfer studies. The experiments and calculations are based on a non-rotating machine and a wind tunnel using the same rotor coil(s). CHT-based thermal calculations provide not only reliable results compared to experimental work and lumped parameter thermal circuits with adjusted aggregate parameters, but also insight related to pressure and cooling flow distribution, thermal loads, and cooling integration issues that are necessary for the development of high power density and reliable electrical machines. The results of the air-cooling integration show that the desired high current density is achievable at the expense of high cooling intensity, where the air velocity ranges from 15 to 30 m/s and 30 to 55 m/s, distinguishing the air velocity of the hollow conductor and bypass channel, compared to the same coil in an electric machine and a wind tunnel at the similar thermal load and limit. Since the hot spot location depends on cooling integration and cooling intensity, modeling and estimating the cooling flow is essential in the development of wound-field synchronous machines. Full article
(This article belongs to the Section Electrical Machines and Drives)
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27 pages, 18817 KiB  
Article
Research on Bolt Loosening Mechanism Under Sine-on-Random Coupling Vibration Excitation
by Jiangong Du, Yuanying Qiu and Jing Li
Machines 2025, 13(2), 80; https://doi.org/10.3390/machines13020080 - 23 Jan 2025
Viewed by 765
Abstract
This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum [...] Read more.
This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum Density (PSD) expression in the frequency domain and superimposes it with random vibration excitation to obtain the SOR synthesized excitation spectrum. Then, by means of a four-bolt fastened structure, the bolt loosening mechanisms under both the sine and random vibration excitation are deeply studied, respectively. Ultimately, based on the time–frequency conversion method of SOR synthesized excitation, the bolt loosening responses of the structure under SOR excitation with different tightening torques are analyzed. Furthermore, a three-stage criterion including the Steady Stage, Transition Stage, and Loosen Stage for bolt loosening under SOR excitation is revealed, and the relationship among the SOR synthesized vibration responses and the two forms of single vibration responses is explored based on a corrective energy superposition method by introducing the weight factors of the two single vibration responses under different tightening torques. Finally, test verifications for the four-bolt fastened structure are conducted and good consistencies with the results of the Finite Element Analysis (FEA) are shown. This study provides valuable insights into the detection and prevention of loosening in bolted connection structures under multi-source vibration environments and has important engineering reference significance. Full article
(This article belongs to the Special Issue Design and Experimental Activity of Testing Machines and Mechanical Test Rigs)
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20 pages, 11567 KiB  
Article
Experimental Acoustic Noise and Sound Quality Characterization of a Switched Reluctance Motor Drive with Hysteresis and PWM Current Control
by Moien Masoumi and Berker Bilgin
Machines 2025, 13(2), 82; https://doi.org/10.3390/machines13020082 - 23 Jan 2025
Cited by 1 | Viewed by 701
Abstract
This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of [...] Read more.
This paper presents an experimental characterization of acoustic noise and sound quality in a 12/8 Switched Reluctance Motor (SRM) using hysteresis and Pulse Width Modulation (PWM) current control techniques. To overcome the limitations of traditional sound power measurements and enhance the accuracy of acoustic noise evaluation, a setup is applied for calculating sound power based on sound intensity measurements. The study provides a detailed description of the intensity probe-holding fixture, the hardware configuration for acoustic noise experiments, and the software setup tailored to specific measurement requirements. The acoustic noise characteristics of the motor are assessed at various operating points using two distinct current control methods: hysteresis current control with a variable switching frequency of up to 20 kHz and PWM current control with a fixed switching frequency of 12.5 kHz. Measurements of sound pressure and sound intensity enable the calculation of sound power and sound quality metrics under different operating conditions. Furthermore, the study investigates the influence of various factors on the motor’s sound power levels and sound quality. The findings provide valuable insights into the contributions of these factors to acoustic noise characteristics and offer a foundation for improving the motor’s acoustic behavior during the design and control stages. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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18 pages, 3333 KiB  
Review
A Short Review: Tribology in Machining to Understand Conventional and Latest Modeling Methods with Machine Learning
by Seisuke Kano
Machines 2025, 13(2), 81; https://doi.org/10.3390/machines13020081 - 23 Jan 2025
Viewed by 1093
Abstract
Tribology plays a critical role in machining technologies. Friction is an essential factor in processes such as composite material machining and bonding. This short review highlights the recent advancements in controlling and leveraging tribological phenomena in machining. For instance, high-precision machining is increasingly [...] Read more.
Tribology plays a critical role in machining technologies. Friction is an essential factor in processes such as composite material machining and bonding. This short review highlights the recent advancements in controlling and leveraging tribological phenomena in machining. For instance, high-precision machining is increasingly relying on the in situ observation and real-time measurement of tools, test specimens, and machining equipment for effective process control. Modern engineering materials often incorporate functional materials in metastable states, such as composites of dissimilar materials, rather than conventional stable-phase materials. In these cases, tribological effects during machining can impede precision. On the other hand, the friction in additive manufacturing demonstrates a constructive application of tribology. Traditionally, understanding and mitigating these tribological phenomena have involved developing physical and chemical models for individual factors and using simulations to inform decisions. However, accurately predicting system behavior has remained challenging due to the complex interactions between machine components and the variations between initial and operational (or deteriorated) states. Recent innovations have introduced data-driven approaches that predict system behavior without the need for detailed models. By integrating advanced monitoring technologies and machine learning, these methods enable real-time predictions within controllable parameters using live data. This shift opens new possibilities for achieving more precise and adaptive machining control. Full article
(This article belongs to the Special Issue Tribology in Manufacturing: Bottlenecks and Advances)
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25 pages, 24599 KiB  
Article
MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions
by Wenhan Huang, Xiangfeng Zhang, Hong Jiang, Zhenfa Shao and Yu Bai
Machines 2025, 13(1), 71; https://doi.org/10.3390/machines13010071 - 20 Jan 2025
Viewed by 767
Abstract
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on [...] Read more.
In complex industrial scenarios, high-quality fault data of rotating machinery are scarce and costly to collect. Therefore, small sample fault diagnosis needs further research. To solve this problem, in this work is proposed a minimum variance auxiliary classifier generation adversarial network based on a multi-scale convolutional block attention mechanism. Firstly, the multi-scale convolutional block attention mechanism is designed to extract multi-scale information and perform weighted fusion to enhance the ability of the model to capture effective features. Secondly, the minimum variance term is designed to minimize the variance of sample distribution, so that the generated samples are distributed more evenly in the feature space, avoiding the problem of pattern collapse. Finally, the objective function is reconstructed by independent classification loss to improve the ability of model data generation. Experimental results on CWRU and gearbox datasets validate the effectiveness and reliability of the proposed method. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 9423 KiB  
Article
A Common DC Bus Circulating Current Suppression Method for Motor Emulators of New Energy Vehicles
by Haonan Sun, Dafang Wang, Qi Li and Yingkang Qin
Machines 2025, 13(1), 51; https://doi.org/10.3390/machines13010051 - 13 Jan 2025
Viewed by 679
Abstract
In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost [...] Read more.
In contrast to the conventional topology, wherein the Device Under Test (DUT) controller and the electric motor emulator (EME) are powered by the DC (Direct Current) voltage source independently, the common DC bus topology necessitates a single power supply. This reduces the cost and complexity of the motor emulator system, making it more favorable for large-scale industrial applications. However, this topology introduces significant circulating current issues in the system. A common DC bus circulating current suppression method is proposed in this paper for the motor emulator. First, the mechanism of zero-sequence circulating current generation in the common DC bus topology is analyzed and the expression for the system’s zero-sequence voltage difference is derived. Then, a control method based on a Hybrid PWM (Pulse Width Modulation) strategy that unifies SPWM (SIN Pulse Width Modulation) and SVPWM (Space Vector Pulse Width Modulation) is proposed, which has been shown to be effective in suppressing the zero-sequence circulating current in a motor emulator system with a common DC bus topology. The proposed control method has been experimentally validated using a motor emulator system. Full article
(This article belongs to the Special Issue New Trends in Reliability and Lifetime Improvement in Power-Electronic-Controlled Machines and Devices)
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31 pages, 21587 KiB  
Article
Bearing Fault Feature Extraction Method Based on Adaptive Time-Varying Filtering Empirical Mode Decomposition and Singular Value Decomposition Denoising
by Xuezhuang E, Wenbo Wang and Hao Yuan
Machines 2025, 13(1), 50; https://doi.org/10.3390/machines13010050 - 13 Jan 2025
Viewed by 645
Abstract
Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) [...] Read more.
Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 6899 KiB  
Article
Influence of Potting Radius on the Structural Performance and Failure Mechanism of Inserts in Sandwich Structures
by Filippos Filippou and Alexis Τ. Kermanidis
Machines 2025, 13(1), 34; https://doi.org/10.3390/machines13010034 - 7 Jan 2025
Viewed by 781
Abstract
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the [...] Read more.
In this study, the mechanical performance and failure modes of cold-potted inserts within sandwich structures were examined, focusing on the influence of the potting radius, while maintaining constant insert radius and specimen characteristics. In this research, destructive testing was used to evaluate the pull out, load-carrying capacity, and failure mechanisms of the inserts. The methods of stiffness degradation and acoustic emissions (AE) were employed for structural health monitoring to capture real-time data on failure progression, including core buckling, core rupture, and skin delamination. The results indicated that increasing the potting radius significantly altered the failure modes and critical failure load of the insert system. A critical potting radius was identified where maximum stiffness was achieved. Beyond this point, insert fracture became the dominant failure mode, with minimal damage to the surrounding core and CFRP skins. Larger potting radii also led to reduced displacement at failure, increased ultimate loads, and elevated stiffness, which were maintained until sudden structural failure. Through detailed isolation and observation of each failure event and with the use of AE data, precise identification of system damage in real time was allowed, offering insights into the progression and causes of failure. Full article
(This article belongs to the Special Issue State of the Art in the Field of Machine and System Testing to Assist in the Diagnosis and Prognosis of Failures)
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18 pages, 4724 KiB  
Article
A Wearable Stiffness-Rendering Haptic Device with a Honeycomb Jamming Mechanism for Bilateral Teleoperation
by Thomas M. Kwok, Bohan Zhang and Wai Tuck Chow
Machines 2025, 13(1), 27; https://doi.org/10.3390/machines13010027 - 6 Jan 2025
Viewed by 1087
Abstract
This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 [...] Read more.
This paper addresses the challenge of providing kinesthetic feedback in bilateral teleoperation by designing a wearable, lightweight (20 g), and compact haptic device, the HJ-Haptic, utilizing a honeycomb jamming mechanism for object stiffness rendering. The HJ-Haptic device can vary its stiffness, from 1.15 N/mm to 2.64 N/mm, using a 30 kPa vacuum pressure. We demonstrate its implementation in a teleoperation framework, enabling operators to adjust grip force based on a reliable haptic feedback on object stiffness. A three-point flexural test on the honeycomb jamming mechanism and teleoperated object-grasping tasks were conducted to evaluate the device’s functionality. Our experiments demonstrated a small RMSE and strong correlations in teleoperated motion, stiffness rendering, and interaction force feedback. The HJ-Haptic effectively adjusts its stiffness in response to real-time gripper feedback, mimicking the sensation of direct object grasping with hands. The device’s use of vacuum pressure ensures operator safety by preventing dangerous outcomes in case of gas leakage or material failure. Incorporating the HJ-Haptic into the teleoperation framework provided the reliable perception of object stiffness and stable teleoperation. This study highlights the potential of the honeycomb jamming mechanism for enhancing haptic feedback in various applications, including teleoperation scenarios, as well as interactions with extended-reality environments. Full article
(This article belongs to the Special Issue Advances and Challenges in Wearable Robotics)
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29 pages, 6463 KiB  
Article
A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing
by Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim and Jongpil Jeong
Machines 2025, 13(1), 21; https://doi.org/10.3390/machines13010021 - 31 Dec 2024
Cited by 1 | Viewed by 1277
Abstract
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions [...] Read more.
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. Manufacturing processes often encounter malfunctions or defective parts that disrupt production and compromise product quality. However, collecting and labeling sufficient data to detect anomalies is time-intensive, and abnormal data are rare, leading to data imbalances. The FS-GAN model leverages few-shot learning to enable accurate predictions with minimal data and uses the generative capabilities of AnoGAN to mitigate the scarcity of abnormal data by generating synthetic normal data. Experimental results demonstrate that FS-GAN outperforms existing models in terms of accuracy and learning speed, even with limited datasets, effectively addressing the data imbalance problem inherent in manufacturing. The model reduces dependency on extensive data collection and labeling efforts, making it suitable for real-world applications. Through reliable and efficient anomaly detection, FS-GAN contributes to production reliability, product quality, and operational efficiency in smart factories. This study highlights the potential of FS-GAN to provide a cost-effective and high-performance solution to the challenges of anomaly detection in the manufacturing industry. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 816 KiB  
Article
Condition Monitoring of Marine Diesel Lubrication System Based on an Optimized Random Singular Value Decomposition Model
by Shuxia Ye, Bin Da, Liang Qi, Han Xiao and Shankai Li
Machines 2025, 13(1), 7; https://doi.org/10.3390/machines13010007 - 25 Dec 2024
Viewed by 787
Abstract
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, [...] Read more.
As modern marine diesel engine systems become increasingly complex, effective condition monitoring methods are essential for ensuring optimal performance and preventing anomalies. This paper proposes a data-driven condition monitoring approach specifically designed for the lubrication system of marine diesel engines. Unlike traditional methods, the proposed approach eliminates the need for explicit modeling and leverages a novel optimization algorithm for data denoising. Additionally, a new noise-resistant monitoring index is introduced to enhance monitoring reliability. The paper is structured into two main sections for validation. The first section addresses advanced data preprocessing, where the Improved Sparrow Search Algorithm (ISSA) is employed to optimize the parameters of Random Singular Value Decomposition (RSVD). This step effectively minimizes noise, reduces manual intervention, and handles high-dimensional data. The second section focuses on analyzing the data characteristics using the Random Matrix Theory (RMT) and establishing novel condition monitoring indicators to achieve more reliable monitoring outcomes. The proposed methodology captures the intricate relationships among key variables within the system, providing a more robust framework for condition monitoring. Applied to a marine diesel engine lubrication system, the method demonstrates significant improvements in noise immunity and monitoring reliability. Comparative analyses of condition monitoring models before and after denoising reveal that the relative error of the proposed monitoring index under varying noise amplitudes is within 1%, substantially lower than that of other indices. Furthermore, the monitoring accuracy is improved by 4.95% when the proposed index is employed for system condition monitoring. Full article
(This article belongs to the Special Issue Advanced Techniques for Fault Detection, Diagnosis, and Prognostics in Machinery)
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12 pages, 6442 KiB  
Article
Design and Validation of an Improved Rotational Variable Stiffness Mechanism
by Carl Nelson, Kasey Moomau and Yucheng Li
Machines 2025, 13(1), 6; https://doi.org/10.3390/machines13010006 - 25 Dec 2024
Cited by 1 | Viewed by 753
Abstract
In various aspects of robotics, including human–robot interaction, the ability to dynamically adjust the apparent stiffness of an interaction (e.g., between the robot and its environment or between the robot and its payload) has become an important capability. Various means have been developed [...] Read more.
In various aspects of robotics, including human–robot interaction, the ability to dynamically adjust the apparent stiffness of an interaction (e.g., between the robot and its environment or between the robot and its payload) has become an important capability. Various means have been developed in recent years to achieve this, notable among them the so-called variable lever devices. In this paper, we present a new variable lever mechanism based on a gear–rack pair. This unique design combines the functionality of the lever itself with that of the stiffness-adjustment transmission. We show through simulations and hardware experiments the relatively large resulting range of achievable stiffness adjustment and efficient operation. Full article
(This article belongs to the Special Issue Selected Papers from MEDER 2024: Advances of Mechanism Design for Robotic Machines)
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15 pages, 8534 KiB  
Article
Development of Digital Flow Valve Applied to Aero-Engine Fuel Control and Research on Performance of Its Flow Characteristics
by Yuesong Li
Machines 2024, 12(12), 936; https://doi.org/10.3390/machines12120936 - 20 Dec 2024
Viewed by 671
Abstract
Digital valves have strong anti-pollution ability and good linearity, so they are more suitable for aero-engine fuel control. However, for high-precision flow control, incremental digital valves require a high-precision, high-dynamic servomotor drive; binary-coded digital valves require many on/off valves; and high-speed switching digital [...] Read more.
Digital valves have strong anti-pollution ability and good linearity, so they are more suitable for aero-engine fuel control. However, for high-precision flow control, incremental digital valves require a high-precision, high-dynamic servomotor drive; binary-coded digital valves require many on/off valves; and high-speed switching digital valves can cause flow shock and pulsation. In this study, an aero-engine fuel control decimal-coded digital flow valve was developed, which not only has the advantages of digital valves but also avoids the above problems. Firstly, the structure and operation principle of the decimal-coded digital flow valve is introduced; then, its model is established based on Simulink/Simcape, and its flow characteristics are simulated and analyzed. Then, experiments on the flow characteristics are presented. The simulation and experiment show that under a supply pressure of 1 MPa, 2 MPa, and 3 MPa, the maximum flow of the decimal-coded digital valve is 11.4457 L/min, 16.3719 L/min, and 19.3733 L/min, and the control accuracy is 0.0775 L/min, 0.1086 L/min, and 0.1294 L/min, respectively. In addition, it has very good linearity, and the settling time is less than 0.09s. Full article
(This article belongs to the Section Automation and Control Systems)
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25 pages, 1305 KiB  
Article
Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data
by Matthew Alberts, Sam St. John, Simon Odie, Anahita Khojandi, Bradley Jared, Tony Schmitz, Jaydeep Karandikar and Jamie B. Coble
Machines 2024, 12(12), 923; https://doi.org/10.3390/machines12120923 - 17 Dec 2024
Cited by 2 | Viewed by 954
Abstract
Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due [...] Read more.
Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. Full article
(This article belongs to the Special Issue Application of Sensing Measurement in Machining)
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15 pages, 3412 KiB  
Article
Prediction of Fretting Wear Lifetime of a Coated System
by Kyungmok Kim
Machines 2024, 12(12), 910; https://doi.org/10.3390/machines12120910 - 11 Dec 2024
Viewed by 704
Abstract
This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of [...] Read more.
This article proposes a model of predicting the fretting wear lifetime of a low-friction coating. The proposed model incorporates multiple factors that influence the fretting wear damage of coatings: the imposed contact load, imposed average velocity, coating hardness, and initial surface roughness of counterparts. The fretting wear lifetime of coatings, defined as the number of cycles critical to friction coefficient evolution, was collected from the literature. For the purpose of identifying parameters in the model, experimental fretting wear lifetime data were analyzed. The results show that the fretting wear lifetime of a coating can be described by an inverse power law regarding the contact load, imposed average velocity, and initial surface roughness of counterparts. In contrast, the fretting wear lifetime of a coating was observed to increase with increased coating hardness. It was observed that the exponents of the inverse power law varied with respect to the type of coating. The proposed fretting wear lifetime model enables the prediction of coating lifetime under various fretting conditions. Full article
(This article belongs to the Special Issue Design and Characterization of Engineered Bearing Surfaces)
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23 pages, 5495 KiB  
Article
Optimization of Machining Parameters for Reducing Drum Shape Error Phenomenon in Wire Electrical Discharge Machining Processes
by Shih-Ming Wang, Li-Jen Hsu, Hariyanto Gunawan and Ren-Qi Tu
Machines 2024, 12(12), 908; https://doi.org/10.3390/machines12120908 - 10 Dec 2024
Viewed by 718
Abstract
Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of [...] Read more.
Machining thicker workpieces in the process of Wire Electrical Discharge Machining (WEDM) can result in a concave phenomenon known as a “drum shape error” due to the vibration of wires and accumulation of debris, which leads to secondary discharge in the middle of the workpiece. Reducing the drum shape error typically requires a longer finishing process. Finding a balance between precision and machining time efficiency has become a challenge for modern machining shops. This study employed experimental analysis to investigate the effect of individual parameters on the shape error and machining removal rate (MRR). Key influential parameters, including open voltage (OV), pulse ON time (ON), pulse OFF time (OFF), and servo voltage (SV), were chosen for data collection using full factorial and Taguchi orthogonal arrays. Regression analysis was conducted to establish multiple regression equations. These equations were used to develop optimization rules, and subsequently, a user-friendly human–machine interface was developed using C# based on these optimization rules to create a shape error and MRR optimization system. The system can predict the optimal parameter combinations to minimize the shape error and increase the MRR. The results of the verification experiments showed that the prediction accuracy can reach 94.7% for shape error and 99.2% for MRR. Additionally, the shape error can be minimized by up to 40%. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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18 pages, 10226 KiB  
Article
Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis
by Wasim Zaman, Muhammad Farooq Siddique, Saif Ullah, Faisal Saleem and Jong-Myon Kim
Machines 2024, 12(12), 905; https://doi.org/10.3390/machines12120905 - 10 Dec 2024
Cited by 2 | Viewed by 1135
Abstract
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification [...] Read more.
Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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23 pages, 10631 KiB  
Article
Multi-Agent Reinforcement Learning Tracking Control of a Bionic Wheel-Legged Quadruped
by Rezwan Al Islam Khan, Chenyun Zhang, Zhongxiao Deng, Anzheng Zhang, Yuzhen Pan, Xuan Zhao, Huiliang Shang and Ruijiao Li
Machines 2024, 12(12), 902; https://doi.org/10.3390/machines12120902 - 9 Dec 2024
Cited by 1 | Viewed by 1927
Abstract
This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating [...] Read more.
This paper presents a novel approach to developing control strategies for mobile robots, specifically the Pegasus, a bionic wheel-legged quadruped robot with unique chassis mechanics that enable four-wheel independent steering and diverse gaits. A multi-agent (MA) reinforcement learning (RL) controller is proposed, treating each leg as an independent agent with the goal of autonomous learning. The framework involves a multi-agent setup to model torso and leg dynamics, incorporating motion guidance optimization signal in the policy training and reward function. By doing so, we address leg schedule patterns for the complex configuration of the Pegasus, the requirement for various gaits, and the design of reward functions for MA-RL agents. Agents were trained using two variations of policy networks based on the framework, and real-world tests show promising results with easy policy transfer from simulation to the actual hardware. The proposed framework models acquired higher rewards and converged faster in training than other variants. Various experiments on the robot deployed framework showed fast response (0.8 s) under disturbance and low linear, angular velocity, and heading error, which was 2.5 cm/s, 0.06 rad/s, and 4°, respectively. Overall, the study demonstrates the feasibility of the proposed MA-RL control framework. Full article
(This article belongs to the Special Issue Design and Application of Bionic Robots)
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22 pages, 8560 KiB  
Article
Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems
by Tingting Wu, Hongliang Song, Hongli Gao, Zongshen Wu and Feifei Han
Machines 2024, 12(12), 895; https://doi.org/10.3390/machines12120895 - 6 Dec 2024
Viewed by 900
Abstract
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning [...] Read more.
Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Intelligent Fault Diagnosis)
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17 pages, 9200 KiB  
Article
Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network
by Hehua Yan, Jinbiao Tan, Yixiong Luo, Shiyong Wang and Jiafu Wan
Machines 2024, 12(12), 891; https://doi.org/10.3390/machines12120891 - 6 Dec 2024
Cited by 1 | Viewed by 790
Abstract
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured [...] Read more.
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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14 pages, 9345 KiB  
Article
Effect of Oil Film Radial Clearances on Dynamic Characteristics of Variable Speed Rotor with Non-Concentric SFD
by Weijian Nie, Xiaoguang Yang, Guang Tang, Qicheng Zhang and Ge Wang
Machines 2024, 12(12), 882; https://doi.org/10.3390/machines12120882 - 5 Dec 2024
Viewed by 694
Abstract
Variable-speed aircraft engines require the power turbine rotor to operate stably within a wide range of output speeds, posing a challenge for rotor vibration reduction design. Non-concentric squeeze film dampers (NCSFDs) have been widely used in rotor vibration reduction design due to their [...] Read more.
Variable-speed aircraft engines require the power turbine rotor to operate stably within a wide range of output speeds, posing a challenge for rotor vibration reduction design. Non-concentric squeeze film dampers (NCSFDs) have been widely used in rotor vibration reduction design due to their simple structure. However, comprehensive research on the matching and applicability of NCSFDs under varying operating speeds is lacking. Therefore, this paper investigates the influence of oil film radial clearances on the dynamic characteristics of a variable-speed rotor system with an NCSFD, examining its suitability across variable speeds. This study introduces the principle of equivalent rotor dynamics similarity design, demonstrating good consistency between simulated and real rotor dynamic characteristics, with a radial clearance of 0.10 mm being deemed optimal. The vibration response variation in the rotor at a fixed speed within the range of 0.51 n to 1.0 n does not exceed 4 μm, and the vibration acceleration variation does not exceed 0.04 g, indicating a wide, stable operating speed range. This study can be helpful for the engineering design and vibration reduction design of variable-speed rotors in aircraft engines. Full article
(This article belongs to the Special Issue Power and Propulsion Engineering)
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38 pages, 14107 KiB  
Review
Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects
by Shu Wang and Roger J. Jiao
Machines 2024, 12(12), 873; https://doi.org/10.3390/machines12120873 - 2 Dec 2024
Cited by 2 | Viewed by 1292
Abstract
This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely [...] Read more.
This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Intelligent Manufacturing)
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25 pages, 4153 KiB  
Article
Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels
by Sajad Saraygord Afshari
Machines 2024, 12(12), 856; https://doi.org/10.3390/machines12120856 - 27 Nov 2024
Cited by 1 | Viewed by 1152
Abstract
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to [...] Read more.
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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19 pages, 7892 KiB  
Article
Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks
by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park and Sangjin Maeng
Machines 2024, 12(12), 843; https://doi.org/10.3390/machines12120843 - 25 Nov 2024
Viewed by 3034
Abstract
Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is [...] Read more.
Bearings are vital components in machinery, and their malfunction can result in equipment damage and reduced productivity. As a result, considerable research attention has been directed toward the early detection of bearing faults. With recent rapid advancements in machine learning algorithms, there is increasing interest in proactively diagnosing bearing faults by analyzing signals obtained from bearings. Although numerous studies have introduced machine learning methods for bearing fault diagnosis, the high costs associated with sensors and data acquisition devices limit their practical application in industrial environments. Additionally, research aimed at identifying the root causes of faults through diagnostic algorithms has progressed relatively slowly. This study proposes a cost-effective monitoring system to improve economic feasibility. Its primary benefits include significant cost savings compared to traditional high-priced equipment, along with versatility and ease of installation, enabling straightforward attachment and removal. The system collects data by measuring the vibrations of both normal and faulty bearings under various operating conditions on a test bed. Using these data, a deep neural network is trained to enable real-time feature extraction and classification of bearing conditions. Furthermore, an explainable AI technique is applied to extract key feature values identified by the fault classification algorithm, providing a method to support the analysis of fault causes. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 3622 KiB  
Article
A Soft Start Method for Doubly Fed Induction Machines Based on Synchronization with the Power System at Standstill Conditions
by José M. Guerrero, Kumar Mahtani, Itxaso Aranzabal, Julen Gómez-Cornejo, José A. Sánchez and Carlos A. Platero
Machines 2024, 12(12), 847; https://doi.org/10.3390/machines12120847 - 25 Nov 2024
Cited by 1 | Viewed by 887
Abstract
Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or [...] Read more.
Due to their exceptional operational versatility, doubly fed induction machines (DFIM) are widely employed in power systems comprising variable renewable energy-based electrical generation sources, such as wind farms and pumped-storage hydropower plants. However, their starting and grid synchronization methods require numerous maneuvers or additional components, making the process challenging. In this paper, a soft start method for DFIM, inspired by the traditional synchronization method of synchronous machines, is proposed. This method involves matching the frequencies, voltages, and phase angles on both sides of the main circuit breaker, by adjusting the excitation through the controlled power converter at standstill conditions. Once synchronization is achieved, the frequency is gradually reduced to the rated operational levels. This straightforward starting method effectively suppresses large inrush currents and voltage sags. The proposed method has been validated through computer simulations and experimental tests, yielding satisfactory results. Full article
(This article belongs to the Section Electrical Machines and Drives)
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16 pages, 8804 KiB  
Article
Research on Unbalanced Vibration Characteristics and Assembly Phase Angle Probability Distribution of Dual-Rotor System
by Hui Li, Changzhi Shi, Xuejun Li, Mingfeng Li and Jie Bian
Machines 2024, 12(12), 842; https://doi.org/10.3390/machines12120842 - 24 Nov 2024
Viewed by 704
Abstract
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, [...] Read more.
This paper addresses the complex issue of vibration response characteristics resulting from the unbalanced assembly of the double rotors in the 31F aero-engine. The study investigates the vibration response behavior of the dual-rotor system through the adjustment of rotor assembly phase angle. Initially, a dynamic model of the four-disk, five-pivot dual-rotor system is established, with its natural frequencies and vibration modes verified. The influence of size and the position of the unbalance on the vibration amplitude in the dual-rotor system is analyzed. Additionally, the probability distribution of the assembly phase angles for both the compressor and turbine sections of the low-pressure rotor is examined. The results indicate that for the low-pressure rotor exhibiting excessive vibration, adjusting the assembly phase angle of the rotors’ system’s compressor or the turbine section by 180 degrees leads to a vibration qualification rate of 70.1435%. This finding is consistent with the observations from the field experience method used in the former Soviet Union. Finally, corresponding experimental verification is conducted. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
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18 pages, 3422 KiB  
Article
Use of Image Recognition and Machine Learning for the Automatic and Objective Evaluation of Standstill Marks on Rolling Bearings
by Markus Grebe, Alexander Baral and Dominik Martin
Machines 2024, 12(12), 840; https://doi.org/10.3390/machines12120840 - 23 Nov 2024
Viewed by 773
Abstract
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into [...] Read more.
One main research area of the Competence Centre for Tribology is so-called standstill marks (SSMs) at roller bearings that occur if the bearing is exposed to vibrations or performs just micromovements. SSMs obtained from experiments are usually photographed, evaluated and manually categorized into six classes. An internal project has now investigated the extent to which this evaluation can be automated and objectified. Images of standstill marks were classified using convolutional neural networks that were implemented with the deep learning library Pytorch. With basic convolutional neural networks, an accuracy of 70.19% for the classification of all six classes and 83.65% for the classification of pairwise classes was achieved. Classification accuracies were improved by image augmentation and transfer learning with pre-trained convolutional neural networks. Overall, an accuracy of 83.65% for the classification of all six standstill mark classes and 91.35% for the classification of pairwise classes was achieved. Since 16 individual marks are generated per test run in a typical quasi standstill test (QSST) of the CCT and the deviation in the prediction of the classification is a maximum of one school grade, the accuracy achieved is already sufficient to carry out a reliable and objective evaluation of the markings. Full article
(This article belongs to the Special Issue Remaining Useful Life Prediction for Rolling Element Bearings)
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41 pages, 7143 KiB  
Review
Overview of IoT Security Challenges and Sensors Specifications in PMSM for Elevator Applications
by Eftychios I. Vlachou, Vasileios I. Vlachou, Dimitrios E. Efstathiou and Theoklitos S. Karakatsanis
Machines 2024, 12(12), 839; https://doi.org/10.3390/machines12120839 - 22 Nov 2024
Cited by 1 | Viewed by 1866
Abstract
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being [...] Read more.
The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. Full article
(This article belongs to the Special Issue Advancements in Condition Monitoring of Electric Motors: Integrating Digital Twins, AI, and IoT for Enhanced Operational Efficiency, Fault Diagnosis, and Cybersecurity)
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62 pages, 17046 KiB  
Review
A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
by Haoyuan Zhang, Shanglei Jiang, Defeng Gao, Yuwen Sun and Wenxiang Bai
Machines 2024, 12(12), 833; https://doi.org/10.3390/machines12120833 - 21 Nov 2024
Cited by 3 | Viewed by 3127
Abstract
Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, [...] Read more.
Tool wear is an inevitable phenomenon in the machining process. By monitoring the wear state of a tool, the machining system can give early warning and make advance decisions, which effectively ensures improved machining quality and production efficiency. In the past two decades, scholars have conducted extensive research on tool wear monitoring (TWM) and obtained a series of remarkable research achievements. However, physics-based models have difficulty predicting tool wear accurately. Meanwhile, the diversity of actual machining environments further limits the application of physical models. Data-driven models can establish the deep mapping relationship between signals and tool wear, but they only fit trained data well. They still have difficulty adapting to complex machining conditions. In this paper, physics-based and data-driven TWM models are first reviewed in detail, including the factors that affect tool wear, typical data-based models, and methods for extracting and selecting features. Then, tracking research hotspots, emerging physics–data fusion models are systematically summarized. Full article
(This article belongs to the Section Advanced Manufacturing)
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15 pages, 3119 KiB  
Article
Fault Detection in Harmonic Drive Using Multi-Sensor Data Fusion and Gravitational Search Algorithm
by Nan-Kai Hsieh and Tsung-Yu Yu
Machines 2024, 12(12), 831; https://doi.org/10.3390/machines12120831 - 21 Nov 2024
Viewed by 1131
Abstract
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, [...] Read more.
This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enhance diagnostic accuracy, the research employs wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) to extract multi-scale features from vibration signals. These features are subsequently fused, and GSA is used to optimize the high-dimensional fused features, eliminating redundant data and mitigating overfitting. The optimized features are then input into a support vector machine (SVM) for fault classification, with K-fold cross-validation used to assess the model’s generalization capabilities. Experimental results demonstrate that the proposed diagnosis method, which integrates multi-sensor data fusion with GSA optimization, significantly improves fault diagnosis accuracy compared to methods using single-sensor signals or unoptimized features. This improvement is particularly notable in multi-class fault scenarios. Additionally, GSA’s global search capability effectively addresses overfitting issues caused by high-dimensional data, resulting in a diagnostic model with greater reliability and accuracy across various fault conditions. Full article
(This article belongs to the Special Issue Compensation for Geometric Errors and Improvement in Accuracy Through Innovative Design and Advanced Control Strategies)
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20 pages, 5509 KiB  
Article
Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes
by Xiaoping Li, Yujie Sun, Xinyue Liu and Shaoxuan Zhang
Machines 2024, 12(11), 801; https://doi.org/10.3390/machines12110801 - 12 Nov 2024
Viewed by 843
Abstract
Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian [...] Read more.
Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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13 pages, 6682 KiB  
Article
Design of a Thermal Performance Test Equipment for a High-Temperature and High-Pressure Heat Exchanger in an Aero-Engine
by Wongeun Yun, Manyeong Ha, Kuisoon Kim and Geesoo Lee
Machines 2024, 12(11), 794; https://doi.org/10.3390/machines12110794 - 10 Nov 2024
Viewed by 1061
Abstract
For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat [...] Read more.
For next-generation power systems, particularly aero-gas turbine engines, ultra-light and highly efficient heat exchangers are considered key enabling technologies for realizing advanced cycles. Consequently, the development of efficient and accurate aero-engine heat exchanger test equipment is essential to support future gas turbine heat exchanger advancements. This paper presents the development of a high-pressure and high-temperature (HPHT) heat exchanger test facility designed for aero-engine heat exchangers. The maximum temperature and pressure of the test facility were configured to simulate the conditions of the last-stage compressor of a large civil engine, specifically 1000 K and 5.5 MPa. These conditions were achieved using multiple electric heater systems in conjunction with an air compression system consisting of three turbo compressor units and a reciprocating compressor unit. A commissioning test was conducted using a compact tubular heat exchanger, and the results indicate that the test facility operates stably and that the measured data closely align with the predicted performance of the heat exchanger. A commissioning test of the tubular heat exchanger showed a thermal imbalance of 1.02% between the high-pressure (HP) and low-pressure (LP) lines. This level of imbalance is consistent with the ISO standard uncertainty of ±2.3% for heat dissipation. In addition, CFD simulation results indicated an average deviation of approximately 1.4% in the low-pressure outlet temperature. The close alignment between experimental and CFD results confirms the theoretical reliability of the test bench. The HPHT thermal performance test facility will be expected to serve as a critical test bed for evaluating heat exchangers for current and future gas turbine applications. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 7033 KiB  
Article
Influence of Distributor Structure on Through-Sea Valve Vibration Characteristics and Improvement Design
by Qingchao Yang, Zebin Li, Aimin Diao and Zhaozhao Ma
Machines 2024, 12(11), 791; https://doi.org/10.3390/machines12110791 - 8 Nov 2024
Viewed by 604
Abstract
To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the [...] Read more.
To address the issue of excessive transient noise during the opening and closing of a sea valve, a method for reducing pressure fluctuations during the opening of the electromagnetic hydraulic distributor has been proposed by analyzing the structure and working principle of the distributor. Based on theoretical calculation and simulation analysis, the size and shape of the buffer slot of the flow hole are determined under the condition that the stable working flow rate remains unchanged. An improved electromagnetic hydraulic distributor is developed and tested. The results indicate that this method can effectively control the opening and closing transient noise of the sea valve. Full article
(This article belongs to the Special Issue Advances in Noises and Vibrations for Machines)
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21 pages, 5379 KiB  
Article
Characterization of Surface Integrity of 3D-Printed Stainless Steel by Successive Grinding and Varied Burnishing Parameters
by Frezgi Tesfom Kebede, Jawad Zaghal and Csaba Felho
Machines 2024, 12(11), 790; https://doi.org/10.3390/machines12110790 - 7 Nov 2024
Cited by 1 | Viewed by 1803
Abstract
Additive manufacturing (AM)’s ability to produce customized products with reduced material wastage and other advantages helped the technology to gain popularity in many industries. However, its poor surface integrity is its weak side, and to overcome this, additional post-processes are essential. Slide diamond [...] Read more.
Additive manufacturing (AM)’s ability to produce customized products with reduced material wastage and other advantages helped the technology to gain popularity in many industries. However, its poor surface integrity is its weak side, and to overcome this, additional post-processes are essential. Slide diamond burnishing, known for its enhancement of surface roughness, residual stress, microhardness, and other properties, was combined with grinding in this research after 3D printing of MetcoAdd 17-4PH-A to mitigate the mentioned shortcomings. This study aimed to analyze the effects of each process on surface roughness, residual stress (both on the surface and in-depth), and microhardness. Workpieces were ground with the same parameters and burnished with four levels of force, feed, and number of passes. The L16 Taguchi experimental design was used to optimize the process parameters and to study their effects. For surface roughness, the optimum parameters were found to be 60 N force, 0.02 m/min feed rate, and three passes. The longitudinal surface residual stress has optimal values at 80 N force, 0.02 m/min feed rate, and four passes. In the case of transverse surface residual stress, the optimal values were 60 N force, 0.17 m/min feed rate, and three passes. Microhardness was maximized with 60 N force, 0.02 m/min feed rate, and one pass. Additionally, the in-depth residual stress for selected surfaces was investigated, and 100 N force showed a deep burnishing effect. Further multi-objective optimization using desirability function analysis found that the optimal parameters for all responses were achieved at the fourth burnishing force level (100 N), the first tool feed level (0.02 m/min), and the fourth number of passes level (four passes). Ultimately, both grinding and burnishing processes exhibited significant enhancements in the measured parameters. Full article
(This article belongs to the Section Advanced Manufacturing)
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21 pages, 16720 KiB  
Article
An Enhanced Spectral Amplitude Modulation Method for Fault Diagnosis of Rolling Bearings
by Zongcai Ma, Yongqi Chen, Tao Zhang and Ziyang Liao
Machines 2024, 12(11), 779; https://doi.org/10.3390/machines12110779 - 6 Nov 2024
Viewed by 637
Abstract
As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. [...] Read more.
As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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29 pages, 53780 KiB  
Article
Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives
by Roberto Guida, Antonio Carlo Bertolino, Andrea De Martin and Massimo Sorli
Machines 2024, 12(11), 776; https://doi.org/10.3390/machines12110776 - 5 Nov 2024
Cited by 1 | Viewed by 2282
Abstract
The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure [...] Read more.
The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 3670 KiB  
Article
Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace
by Wenhong Zhou, Liuzhou Zhong, Weimin Kang, Yuetong Xu, Congcong Luan and Jianzhong Fu
Machines 2024, 12(11), 774; https://doi.org/10.3390/machines12110774 - 4 Nov 2024
Cited by 1 | Viewed by 1082
Abstract
Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study [...] Read more.
Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states. Full article
(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)
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20 pages, 3602 KiB  
Article
Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine
by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis and Evangelos Diamantis
Machines 2024, 12(11), 762; https://doi.org/10.3390/machines12110762 - 30 Oct 2024
Cited by 2 | Viewed by 1106
Abstract
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency [...] Read more.
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 9472 KiB  
Article
A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings
by Miao He, Zhonghua Li and Fangchao Hu
Machines 2024, 12(11), 766; https://doi.org/10.3390/machines12110766 - 30 Oct 2024
Cited by 2 | Viewed by 888
Abstract
Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This [...] Read more.
Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets. Full article
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21 pages, 7354 KiB  
Article
Visual-Inertial Fusion-Based Five-Degree-of-Freedom Motion Measurement System for Vessel-Mounted Cranes
by Boyang Yu, Yuansheng Cheng, Xiangjun Xia, Pengfei Liu, Donghong Ning and Zhixiong Li
Machines 2024, 12(11), 748; https://doi.org/10.3390/machines12110748 - 23 Oct 2024
Viewed by 1222
Abstract
Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo [...] Read more.
Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo’s position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target’s three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system’s measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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15 pages, 2222 KiB  
Article
LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control
by Ruocheng Yin and Juan Ren
Machines 2024, 12(11), 747; https://doi.org/10.3390/machines12110747 - 22 Oct 2024
Viewed by 854
Abstract
This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The [...] Read more.
This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The feedforward controller, a sequence-to-sequence LSTM-based inversion model (invLSTMs2s), is used to compensate for the nonlinearity of the PEA, especially at high frequencies, and is collaboratively integrated with a linear MPC feedback controller, which ensures the PEA position tracking performance at low frequencies. Therefore, the proposed 2DOF controller, namely, invLSTMs2s+MPC, is able to achieve high precision over a broad bandwidth. To validate the proposed controller, the uncertainty of invLSTMs2s is checked such that the integration of an inversion model-based feedforward controller has a positive impact on the trajectory tracking performance compared to feedback control only. Experimental validation on a commercial PEA and comparison with existing approaches demonstrate that high tracking accuracies can be achieved by invLSTMs2s+MPC for various reference trajectories. Moreover, invLSTMs2s+MPC is further demonstrated on a multi-dimensional PEA platform for simultaneous multi-direction positioning control. Full article
(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)
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23 pages, 10799 KiB  
Article
The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
by Jingqi Zhang, Jianxing Zhou, Quanwei Cui, Ning Dong, Hong Jiang and Zhong Fang
Machines 2024, 12(10), 734; https://doi.org/10.3390/machines12100734 - 17 Oct 2024
Viewed by 1142
Abstract
Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study [...] Read more.
Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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17 pages, 11316 KiB  
Article
Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion
by Yue Zheng, Guoqiang Fu, Sen Mu, Caijiang Lu, Xi Wang and Tao Wang
Machines 2024, 12(10), 728; https://doi.org/10.3390/machines12100728 - 15 Oct 2024
Viewed by 1294
Abstract
Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature [...] Read more.
Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors. In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Firstly, the temperature information fusion features are built as inputs to the model, which is based on a self-attention mechanism to assign weights to the temperature information and fuse the features. Secondly, an improved direct normalization-based adaptive matrix approach is proposed, updating the background matrix using an autoencoder and reconstructing the adaptive matrix to realize domain self-adaptation. In addition, for the improved adaptive matrix, a criterion for determining whether the working conditions are transferrable to each other is proposed. The proposed method shows high prediction accuracy while ensuring training efficiency. Finally, thermal error experiments are performed on a VCM850 CNC machine tool. Full article
(This article belongs to the Special Issue Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools)
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22 pages, 7423 KiB  
Article
Advancing UAV Sensor Fault Diagnosis Based on Prior Knowledge and Graph Convolutional Network
by Hui Li, Chaoyin Chen, Tiancai Wan, Shaoshan Sun, Yongbo Li and Zichen Deng
Machines 2024, 12(10), 716; https://doi.org/10.3390/machines12100716 - 10 Oct 2024
Cited by 1 | Viewed by 1058
Abstract
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have [...] Read more.
Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV’s dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. Full article
(This article belongs to the Special Issue Data-Driven Approaches regarding Dynamic Modelling, Diagnostics and Prognostics of Complex Mechanical Structures)
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19 pages, 2951 KiB  
Article
Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity
by Qinghua Cao, Jian Wu, Fuxing Xu, Xinhong Miao, Mingjie Guo and Yuan Chu
Machines 2024, 12(10), 698; https://doi.org/10.3390/machines12100698 - 2 Oct 2024
Viewed by 966
Abstract
Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time [...] Read more.
Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy. Full article
(This article belongs to the Special Issue Modeling, Estimation, Control, and Decision for Intelligent Vehicles)
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17 pages, 13481 KiB  
Article
Detection of Broken Bars in Induction Motors Operating with Closed-Loop Speed Control
by Francesca Muzio, Lorenzo Mantione, Tomas Garcia-Calva, Lucia Frosini and Daniel Morinigo-Sotelo
Machines 2024, 12(9), 662; https://doi.org/10.3390/machines12090662 - 21 Sep 2024
Cited by 2 | Viewed by 1144
Abstract
Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate [...] Read more.
Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252. Full article
(This article belongs to the Section Electrical Machines and Drives)
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