Previous Issue
Volume 13, April
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Volume 13
Issue 5
share announcement

Machines, Volume 13, Issue 5 (May 2025) – 35 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
15 pages, 2907 KiB  
Article
Finite Element Analysis of Post-Buckling Failure in Stiffened Panels: A Comparative Approach
by Jakiya Sultana and Gyula Varga
Machines 2025, 13(5), 373; https://doi.org/10.3390/machines13050373 (registering DOI) - 29 Apr 2025
Abstract
Stiffened panels are extensively used in aerospace applications, particularly in wing and fuselage sections, due to their favorable strength-to-weight ratio under in-plane loading conditions. This research employs the commercial finite element software Ansys- 19 to analysis the critical buckling and ultimate collapse load [...] Read more.
Stiffened panels are extensively used in aerospace applications, particularly in wing and fuselage sections, due to their favorable strength-to-weight ratio under in-plane loading conditions. This research employs the commercial finite element software Ansys- 19 to analysis the critical buckling and ultimate collapse load of an aluminum stiffened panel having a dimension of 1244 mm (Length) × 957 mm (width) × 3.5 mm (thickness), with three stiffener blades located 280 mm away from each other. Both the critical buckling load and post-buckling ultimate failure load of the panel are validated against the experimental data found in the available literature, where the edges towards the length are clamped and simply supported, and the other two edges are free. For nonlinear buckling analysis, a plasticity power law is adopted with a small geometric imperfection of 0.4% at the middle of the panel. After the numerical validation, the investigation is further carried out considering four different lateral pressures, specifically 0.013 MPa, 0.065 MPa, 0.085 MPa, and 0.13 MPa, along with the compressive loading boundary conditions. It was found that even though the pressure application of 0.013 MPa did not significantly impact the critical buckling load of the panel, the ultimate collapse load was reduced by 18.5%. In general, the ultimate collapse load of the panel was severely affected by the presence of lateral pressure while edge compressing. Three opening shapes—namely, square, circular, and rectangular/hemispherical—were also investigated to understand the behavior of the panel with openings. It was found that the openings significantly affected the critical buckling load and ultimate collapse load of the stiffened panel, with the lateral pressure also contributing to this effect. Finally, in critical areas with higher lateral pressure load, a titanium panel can be a good alternative to the aluminum panel since it can provide almost twice to thrice better buckling stability and ultimate collapse load to the panels with a weight nearly 1.6 times higher than aluminum. These findings highlight the significance of precision manufacturing, particularly in improving and optimizing the structural efficiency of stiffened panels in aerospace industries. Full article
(This article belongs to the Special Issue Dimensional Measurement, Analysis, and Control for Precision Manufacturing)
25 pages, 1718 KiB  
Article
Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling
by Pimolkan Piankitrungreang, Kantawatchr Chaiprabha, Worathris Chungsangsatiporn, Chanat Ratanasumawong, Peemdej Chancharoen and Ratchatin Chancharoen
Machines 2025, 13(5), 372; https://doi.org/10.3390/machines13050372 (registering DOI) - 29 Apr 2025
Abstract
This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and [...] Read more.
This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and tool withdrawal. Advanced signal processing techniques, including spectrogram analysis and Fast Fourier Transform, extract dominant frequencies and acoustic patterns, while machine learning algorithms like DBSCAN clustering classify operational states such as cutting, breakthrough, and returning. Experimental studies on materials including acrylic, PTFE, and hardwood reveal distinct acoustic profiles influenced by material properties and drilling conditions. Smoother sound patterns and lower dominant frequencies characterize PTFE drilling, whereas hardwood produces higher frequencies and rougher patterns due to its density and resistance. These findings demonstrate the correlation between acoustic emissions and machining dynamics, enabling non-invasive real-time monitoring and predictive maintenance. As AI power increases, it is expected to extract in-situ process information and achieve higher resolution, enhancing precision in data interpretation and decision-making. A key contribution of this project is the creation of an open sound library for drilling processes, fostering collaboration and innovation in intelligent manufacturing. By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. This AI-ready hardware enhances the accuracy and efficiency of drilling operations, improving quality, reducing tool wear, and minimizing downtime. The research establishes acoustic monitoring as a transformative approach to advancing CNC drilling processes and intelligent manufacturing systems. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
14 pages, 8573 KiB  
Article
A Spatial Five-Bar Linkage as a Tilting Joint of the Breeding Blanket Transporter for the Remote Maintenance of EU DEMO
by Hjalte Durocher, Christian Bachmann, Rocco Mozzillo, Günter Janeschitz and Xuping Zhang
Machines 2025, 13(5), 371; https://doi.org/10.3390/machines13050371 (registering DOI) - 29 Apr 2025
Abstract
The future fusion power plant EU DEMO will generate its own tritium fuel through the use of segmented breeding blankets (BBs), which must be replaced from time to time due to material damage caused by high-energy neutrons from the plasma. A vertical maintenance [...] Read more.
The future fusion power plant EU DEMO will generate its own tritium fuel through the use of segmented breeding blankets (BBs), which must be replaced from time to time due to material damage caused by high-energy neutrons from the plasma. A vertical maintenance architecture has been proposed, using a robotic remote handling tool (transporter) to disengage the 180t and 125t outboard and inboard segments and manipulate them through an upper port. Safe disengagement without damaging the support structures requires the use of high-capacity tilting joints in the transporter. The trolley tilting mechanism (TTM) is proposed as a novel, compact, high-capacity robotic joint consisting of a five-bar spatial mechanism integrated in the BB transporter trolley link. A kinematic model of the TTM is established, and the analytical input–output relationships, including the position-dependent transmission ratio, are derived and used to guide the design and optimization of the mechanism. The model predictions are compared to an ADAMS multibody simulation and to the results of an experiment conducted on a down-scaled prototype, both of which validate the model accuracy. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
16 pages, 949 KiB  
Article
Development of 3D-Printed Vibration Absorbers for Noise Control in Material Removal Processes
by Sungmyung Lee, Haewoon Choi and Jonghyun Kim
Machines 2025, 13(5), 370; https://doi.org/10.3390/machines13050370 (registering DOI) - 29 Apr 2025
Abstract
Material removal processes such as milling, drilling, and turning often generate harmful vibrations that can negatively impact both machine performance and operator safety. Addressing these vibrations at their source or reducing them to safe levels is, therefore, a critical challenge. This study proposes [...] Read more.
Material removal processes such as milling, drilling, and turning often generate harmful vibrations that can negatively impact both machine performance and operator safety. Addressing these vibrations at their source or reducing them to safe levels is, therefore, a critical challenge. This study proposes a practical solution by introducing thin-fin-type vibration-absorbing devices fabricated using 3D printing technology. These devices are designed specifically to mitigate vibration propagation during milling operations. To evaluate their effectiveness, a multi-sensor system comprising sound level meters, a vibrometer, and a vision–acoustic camera was employed to measure sound levels. The results show that the use of fabricated devices can reduce noise levels significantly, from 93 dB (comparable to power tools or a lawn mower) to 74 dB (similar to normal conversation or a busy office). This substantial reduction demonstrates the potential of the proposed devices to enhance workplace safety and acoustic comfort on the shop floor. Full article
(This article belongs to the Special Issue Transforming Classic Machining into Smart Manufacturing)
27 pages, 2234 KiB  
Article
Inheriting Traditional Chinese Bone-Setting: A Framework of Closed Reduction Skill Learning and Dual-Layer Hybrid Admittance Control for a Dual-Arm Bone-Setting Robot
by Zhao Tan, Jialong Zhang, Yahui Zhang, Xu Song, Yan Yu, Guilin Wen and Hanfeng Yin
Machines 2025, 13(5), 369; https://doi.org/10.3390/machines13050369 - 29 Apr 2025
Abstract
Traditional Chinese Bone-setting (TCB) involves complex movements and force feedback, which are critical for effective fracture reduction. However, its practice necessitates the collaboration of highly experienced surgeons, and the availability of expert resources is significantly limited. These challenges have significantly hindered the inheritance [...] Read more.
Traditional Chinese Bone-setting (TCB) involves complex movements and force feedback, which are critical for effective fracture reduction. However, its practice necessitates the collaboration of highly experienced surgeons, and the availability of expert resources is significantly limited. These challenges have significantly hindered the inheritance and dissemination of TCB techniques. The advancement of Learning from Demonstration offers a promising solution for addressing this challenge. In this study, we developed an innovative framework of closed reduction skill learning and dual-layer hybrid admittance control for a dual-arm bone-setting robot, specifically targeting ankle fracture. The framework began with a comprehensive structural design of the robot, incorporating analyses of closed-chain kinematics and the decomposition of internal and external forces. Additionally, we introduced a globally optimal reparameterization algorithm for temporal alignment of demonstrations and extended the Motion/Force Synchronous Kernelized Movement Primitive to learn reduction maneuvers and forces. Furthermore, we designed a dual-layer hybrid admittance controller, consisting of an ankle-layer and a robot- layer. Specifically, we propose a novel adaptive fuzzy variable admittance control strategy for the ankle-layer to achieve accurate tracking of reduction forces, which reduces the RMSE of force tracking along the X-axis by 50.35% compared to the non-fuzzy strategy. The experimental results demonstrated that the framework successfully replicates the human-like bone-setting process and can imitate personalized bone-setting trajectories under expert guidance. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
16 pages, 6172 KiB  
Article
A Novel Dual-Channel Hybrid Attention Model for Wind Turbine Misalignment Fault Diagnosis
by Tong Tong, Xiang Liu, Jia Zhang, Dian Long, Teng Fan and Xiangyang Zheng
Machines 2025, 13(5), 368; https://doi.org/10.3390/machines13050368 - 29 Apr 2025
Abstract
Aiming at the problems of inaccurate feature extraction, slow convergence, and low diagnostic accuracy of wind turbine misalignment fault diagnosis under complex working conditions, this paper proposes an innovative diagnostic method based on two channels of U-Net and ResNet50. The model innovatively introduces [...] Read more.
Aiming at the problems of inaccurate feature extraction, slow convergence, and low diagnostic accuracy of wind turbine misalignment fault diagnosis under complex working conditions, this paper proposes an innovative diagnostic method based on two channels of U-Net and ResNet50. The model innovatively introduces the multi-head attention mechanism (MHA) in the jump connection of the U-Net architecture to form hybrid U-Net and optimizes the feature fusion process with dynamically learnable weights, which significantly enhances the ability to capture local details and key fault features. In the ResNet50 branch, deep global features are fully mined for extraction. To further achieve the co-optimization of global and local information, a shared hybrid expert attention (SHEA) module is proposed. This module achieves efficient integration of features by adaptively fusing the multi-scale local features output from the hybrid U-Net decoder with the deep global features extracted from the ResNet50 backbone network through a dynamic weighting and expert selection mechanism. The multi-scale features optimized by the SHEA module are fed into the classifier for fault type determination. The experimental results show that the method demonstrates excellent convergence speed and 99.64% classification accuracy under complex working conditions, providing an effective solution for the intelligent diagnosis of wind turbine misalignment faults. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
►▼ Show Figures

Figure 1

23 pages, 4864 KiB  
Article
Intelligent Decision Framework for Booster Fan Optimization in Underground Coal Mines: Hybrid Spherical Fuzzy-Cloud Model Approach Enhancing Ventilation Safety and Operational Efficiency
by Shibin Yao, Jian Zhou, Manoj Khandelwal, Abiodun Ismail Lawal, Chuanqi Li, Moshood Onifade and Sangki Kwon
Machines 2025, 13(5), 367; https://doi.org/10.3390/machines13050367 - 29 Apr 2025
Abstract
Optimizing mine fan operations in underground coal mines is important for ensuring proper ventilation, enhancing safety, and improving operational efficiency. A single main ventilation fan is insufficient to meet the ventilation demands of the entire mine. Therefore, it is necessary to consider the [...] Read more.
Optimizing mine fan operations in underground coal mines is important for ensuring proper ventilation, enhancing safety, and improving operational efficiency. A single main ventilation fan is insufficient to meet the ventilation demands of the entire mine. Therefore, it is necessary to consider the addition of booster fans to ensure effective ventilation. However, the selection of booster fans involves multiple influencing factors, and the complex interrelationships among fans remain unclear, making solution selection and risk assessment more challenging. To address this issue, this study proposes an optimization and risk analysis method for booster fan selection based on an improved analytic hierarchy process. This method leverages spherical fuzzy sets to handle uncertainty in the ventilation parameters and cloud models to facilitate probabilistic decision making. Through this model, the important relationships of the influencing factors for fan selection can be systematically determined, allowing for a rational assessment of the performance scores of candidate solutions. It provides a ranking of the alternatives based on their superiority, along with the risk indicators and optimization potentials of the selected solution. Ultimately, the reliability of the chosen model was verified through comparison and validation. This method not only enhances the scientific and rational basis for booster fan selection, reducing the complexity of the selection process, but also provides theoretical support for the optimization of coal mine ventilation systems. This study demonstrates the model’s effectiveness at improving ventilation safety and cost efficiency, making it a valuable tool for modern underground mining operations. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

16 pages, 4222 KiB  
Article
Numerical Simulation of Aerodynamic Characteristics of Trailing Edge Flaps for FFA-W3-241 Wind Turbine Airfoil
by Jiaxin Xu, Zhongyao Ji, Yihuang Zhang, Geye Yao, Yaoru Qian and Zhengzhi Wang
Machines 2025, 13(5), 366; https://doi.org/10.3390/machines13050366 - 29 Apr 2025
Abstract
The blades of wind turbines constitute key components for converting wind energy into electrical energy, and modifications to blade airfoil geometry can effectively enhance aerodynamic performance of wind turbine. The trailing edge flap enables load control on the blades through adjustments of its [...] Read more.
The blades of wind turbines constitute key components for converting wind energy into electrical energy, and modifications to blade airfoil geometry can effectively enhance aerodynamic performance of wind turbine. The trailing edge flap enables load control on the blades through adjustments of its motion and geometric parameters, thereby overcoming limitations inherent in conventional pitch control systems. However, current research primarily emphasizes isolated parametric effects on airfoil performance, with limited exploration of interactions between multiple design variables. This study adopts a numerical simulation approach based on the FFA-W3-241 airfoil of the DTU 10 MW. Geometric deformations are achieved by manipulating flap parameters, and the influence on airfoil aerodynamic performance is analyzed using computational fluid dynamics methods. Investigations are conducted into the effects of flap lengths and deflection angles on airfoil aerodynamic characteristics. The results show the existence of an optimal flap length and deflection angle combination. Specifically, when the flap length is 0.1c and the deflection angle is 10°, the lift-to-drag ratio demonstrates significant improvement under defined operational conditions. These findings offer practical guidance for optimizing wind turbine airfoil designs. Full article
(This article belongs to the Special Issue Cutting-Edge Applications of Wind Turbine Aerodynamics)
►▼ Show Figures

Figure 1

18 pages, 931 KiB  
Article
Dynamic Analysis and Resonance Control of a Tunable Pendulum Energy Harvester Using Cone-Based Continuously Variable Transmission
by Chattarika Uttachee, Surat Punyakaew, Nghia Thi Mai, Md Abdus Samad Kamal, Iwanori Murakami and Kou Yamada
Machines 2025, 13(5), 365; https://doi.org/10.3390/machines13050365 - 29 Apr 2025
Abstract
This paper investigates the design and performance of a tunable pendulum energy harvester (TPEH) integrated with cone continuously variable transmission (CVT) to enhance energy harvesting efficiency in broadband and non-stationary vibrational environments. The cone CVT mechanism enables the tunability of the harvester’s natural [...] Read more.
This paper investigates the design and performance of a tunable pendulum energy harvester (TPEH) integrated with cone continuously variable transmission (CVT) to enhance energy harvesting efficiency in broadband and non-stationary vibrational environments. The cone CVT mechanism enables the tunability of the harvester’s natural frequency, allowing it to dynamically adapt and maintain resonance across varying excitation frequencies. A specific focus is placed on the system’s behavior under chirp signal base excitation, which simulates a time-varying frequency environment. Experimental and analytical approaches are employed to evaluate the system’s dynamic response, energy output, and frequency adaptation capabilities. The results demonstrate that the proposed TPEH system achieves significant energy harvesting performance improvements by leveraging the cone CVT to optimize power generation under resonance conditions. The system is also shown to be effective in maintaining stable operation over a wide range of frequencies, demonstrating its versatility for real-world vibrational energy harvesting applications. This research highlights the importance of tunability in energy harvesting systems and the role of mechanical transmission mechanisms in improving adaptability. The proposed design has strong potential for applications in environments with non-stationary vibrations, such as transportation systems, industrial machinery, and infrastructure monitoring. Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
►▼ Show Figures

Figure 1

18 pages, 5368 KiB  
Article
UAV Real-Time Target Detection and Tracking Algorithm Based on Improved KCF and YOLOv5s_MSES
by Shihai Cao, Ting Wang, Tao Li and Shumin Fei
Machines 2025, 13(5), 364; https://doi.org/10.3390/machines13050364 - 28 Apr 2025
Viewed by 16
Abstract
In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or [...] Read more.
In past decade, even though correlation filter (CF) has achieved rapid developments in the field of unmanned aerial vehicle (UAV) tracking, the discrimination ability between target and background still needs further investigation due to boundary effects. Moreover, when the target is occluded or leaves the view field, it may result in tracking loss of the target. To address these limitations, this work proposes an improved CF tracking algorithm based on some existent ones. Firstly, as for the scale changing of tracking target, an adaptive scale box is proposed to adjustably change the scale of the target box. Secondly, to address boundary effects caused by fast maneuvering, a spatio-temporal search strategy is presented, utilizing spatial context from the target region in the current frame and temporal information from preceding frames. Thirdly, aiming at the problem of tracking loss due to occlusion or out-of-view situations, this work proposes a fusion strategy based on the YOLOv5s_MSES target detection algorithm. Finally, the experimental results show that, compared to the baseline algorithm on the UAV123 dataset, our DP and AUC increased by 14.07% and 14.39%, respectively, and the frames per second (FPS) amounts to 37.5. Additionally, on the OTB100 dataset, the proposed algorithm demonstrates significant improvements in distance precision (DP) metrics across four challenging attributes compared to the baseline algorithm, showing a 12.85% increase for scale variation (SV), 16.45% for fast motion (FM), 18.66% for occlusion (OCC), and 17.09% for out-of-view (OV) scenarios. To sum up, the proposed algorithm not only achieves the ideal tracking effect, but also meets the real-time requirement with higher precision, which means that the comprehensive performance is superior to some existing methods. Full article
(This article belongs to the Section Automation and Control Systems)
►▼ Show Figures

Figure 1

12 pages, 8660 KiB  
Article
Experimental Validation of Positioning Control for an X–Y Table Using S-Curve Velocity Trajectory
by Hsiu-Ming Wu, Chung-Wei Chen and Chau-Yih Nian
Machines 2025, 13(5), 363; https://doi.org/10.3390/machines13050363 - 28 Apr 2025
Viewed by 79
Abstract
As an X–Y table has input saturation constraints or inadequate trajectory planning, the positioning control performance degrades. To overcome this issue, this study proposes an effective anti-integral windup approach based on basic PID control, and then plans a motion trajectory with an S-curve [...] Read more.
As an X–Y table has input saturation constraints or inadequate trajectory planning, the positioning control performance degrades. To overcome this issue, this study proposes an effective anti-integral windup approach based on basic PID control, and then plans a motion trajectory with an S-curve velocity profile to enhance the overall control performance. Finally, the corresponding experiments are conducted to assure the effectiveness of the control framework. The experimental results demonstrate that the proposed control scheme can greatly improve the system positioning precision compared to that without anti-windup when the X–Y table suffers from actuator saturations. Moreover, the corresponding results clearly showcase the superior tracking responses with errors of ±11.23 mm in the X-axis and ±13.63 mm in the Y-axis using the S-curve velocity profile for tracking errors, and ±13.48 mm in the X-axis and ±19.88 mm in the Y-axis applied to the T-curve velocity profile. It is validated and concluded that the proposed control scheme combined with trajectory planning can effectively mitigate integral windup and enhance the positioning precision with the smoother velocity as well as continuous acceleration profiles without vibration. Full article
(This article belongs to the Section Automation and Control Systems)
►▼ Show Figures

Figure 1

27 pages, 4974 KiB  
Systematic Review
Engineering Innovations for Polyvinyl Chloride (PVC) Recycling: A Systematic Review of Advances, Challenges, and Future Directions in Circular Economy Integration
by Alexander Chidara, Kai Cheng and David Gallear
Machines 2025, 13(5), 362; https://doi.org/10.3390/machines13050362 - 28 Apr 2025
Viewed by 129
Abstract
Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed [...] Read more.
Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article
(This article belongs to the Special Issue Innovative Manufacturing Engineering 2024—Processes, Machines, Tooling and Systems Integration)
►▼ Show Figures

Figure 1

25 pages, 4575 KiB  
Article
Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling
by Yuhang Zeng, Ping Lou, Jianmin Hu, Chuannian Fan, Quan Liu and Jiwei Hu
Machines 2025, 13(5), 361; https://doi.org/10.3390/machines13050361 - 27 Apr 2025
Viewed by 127
Abstract
The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state [...] Read more.
The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. Therefore, a human feedback-based large language model-assisted deep reinforcement learning (HFLLMDRL) framework is proposed to solve this problem, in which few-shot prompt engineering by human feedback is utilized to assist in designing instructive reward functions and guiding policy convergence. Additionally, a self-adaptation symbolic visualization Kolmogorov–Arnold Network (KAN) is integrated as the policy network in DRL to enhance state feature representation, thereby improving learning efficiency. Experimental results demonstrate that the proposed framework significantly boosts both learning performance and policy convergence, presenting a novel approach to the JSSP. Full article
(This article belongs to the Section Advanced Manufacturing)
►▼ Show Figures

Figure 1

23 pages, 12486 KiB  
Article
Nonlinear Vibration Analysis of Turbocharger Rotor Supported on Rolling Bearing by Modified Incremental Harmonic Balance Method
by Tangwei Li, Hulun Guo, Zhenyu Cheng, Rixiu Men, Jun Li and Yushu Chen
Machines 2025, 13(5), 360; https://doi.org/10.3390/machines13050360 - 25 Apr 2025
Viewed by 184
Abstract
High-speed rolling bearings exhibit low friction, high mechanical efficiency, low lubrication requirements, and excellent acceleration performance. The replacement of floating ring bearings in turbochargers with rolling bearings is an important tendency for modern turbochargers. However, due to the nonlinearity in rolling bearings, the [...] Read more.
High-speed rolling bearings exhibit low friction, high mechanical efficiency, low lubrication requirements, and excellent acceleration performance. The replacement of floating ring bearings in turbochargers with rolling bearings is an important tendency for modern turbochargers. However, due to the nonlinearity in rolling bearings, the nonlinear vibration characteristics of the turbocharger rotor system need to be clearly revealed. The turbocharger rotor is modeled by a lumped mass model. The nonlinear rolling bearing model is derived using the Hertz contact theory. The vibration responses of the nonlinear system are obtained by the modified incremental harmonic balance (MIHB) method. The results demonstrate that the MIHB method significantly improves computational efficiency compared to the traditional fourth-order Runge–Kutta method for solving this class of problems while also being capable of obtaining complete solution branches of the system. The stability of the responses is determined by the Floquet theory. Based on the present rotor dynamic model, the conical mode and cylindrical mode are found. Resonance peaks at 4.5 × 104 rpm (conical mode) and 1.1 × 105 rpm (bending mode) are identified as critical vibration thresholds. Moreover, the vibration amplitude results show that the resonance peak of the bending mode is mainly due to the nonlinearity of the rolling bearings, which also causes the amplitude jumping phenomenon. Changing the parameters of the rolling bearing could avoid the resonance peak appearing in the working speed range. The amplitude of the system under different rotating speeds could be suppressed by choosing the appropriate parameters of the rolling bearing. Full article
(This article belongs to the Special Issue Nonlinear Dynamics, Vibration Monitoring and Fault Diagnostics in Rotating Systems)
►▼ Show Figures

Figure 1

15 pages, 5685 KiB  
Article
Six-Wheeled Mobile Manipulator for Brush Cleaning in Difficult Areas: Stability Analysis and Grip Condition Estimation
by Giandomenico Di Massa, Stefano Pagano, Ernesto Rocca and Sergio Savino
Machines 2025, 13(5), 359; https://doi.org/10.3390/machines13050359 - 25 Apr 2025
Viewed by 135
Abstract
This paper aims to analyze a six-wheeled mobile manipulator as a solution for brush clearing difficult areas. To this end, a rover with a rocker–bogie suspension system, like those used for space explorations, is considered; the cutting head is moved by a robotic [...] Read more.
This paper aims to analyze a six-wheeled mobile manipulator as a solution for brush clearing difficult areas. To this end, a rover with a rocker–bogie suspension system, like those used for space explorations, is considered; the cutting head is moved by a robotic arm fixed to the rover so that it can reach areas to clean in front of the rover or on its sides. The change of the pose of the robotic arm shifts the centre of mass of the rover and, although the shift is not important, it can be used to improve stability, to overcome an obstacle, or to change the load distribution between the wheels to prevent the wheels from slipping or sinking. Some analyses of the interaction between the rover and robotic arm are reported in this paper. To prevent the rover from entering a low-grip area, the possibility of estimating the grip conditions of the terrain is considered, using the front wheels as tactile sensors. By keeping the rear wheels stationary and gradually increasing the torque on the front wheels, it is possible to evaluate the conditions under which slippage occurs. In case of poor grip, using the other drive wheels, the rover can reverse its direction and look for an alternative path. Full article
(This article belongs to the Special Issue Advances in Mechanism and Machine Science Within the IFIT 2024 Conference)
►▼ Show Figures

Figure 1

18 pages, 9670 KiB  
Article
An Investigation on the Mechanical Characteristics of Railway Locomotive Axle Box Bearings with Sensor-Embedded Slots
by Longkai Wang, Can Hu, Lin Hu, Fengyuan Liu and Hongbin Tang
Machines 2025, 13(5), 358; https://doi.org/10.3390/machines13050358 - 25 Apr 2025
Viewed by 152
Abstract
The intelligent bearing with an embedded sensor is a key technology to realize the running state monitoring of railway locomotive axle box bearings at the source end. To investigate the mechanical properties of axle box bearings with embedded sensor slots, based on nonlinear [...] Read more.
The intelligent bearing with an embedded sensor is a key technology to realize the running state monitoring of railway locomotive axle box bearings at the source end. To investigate the mechanical properties of axle box bearings with embedded sensor slots, based on nonlinear Hertzian contact theory and the bearing fatigue life theory, a mechanical equivalent analysis model with a virtual mandrel is established for double-row tapered roller bearings used in axle boxes with sensor-embedded slots, which integrally considers the effects of external forces. After verifying the mesh independence before and after embedding the sensor slots, the contact load of tapered rollers calculated by the mechanical model is compared with the theoretical solution based on Hertz contact which verifies the validity of the model from the perspective of contact load. The results show that adjusting the grooving depth and axial position has a significant effect on the local stress peak, and an excessive grooving depth or inappropriate axial position will trigger fatigue damage. This study provides a theoretical basis for analyzing the mechanical characteristics of sensor-embedded slots used in railway locomotive axle box bearings. Full article
(This article belongs to the Section Machine Design and Theory)
►▼ Show Figures

Figure 1

25 pages, 722 KiB  
Article
Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors
by Javier de las Morenas, Lidia M. Belmonte and Rafael Morales
Machines 2025, 13(5), 357; https://doi.org/10.3390/machines13050357 - 24 Apr 2025
Viewed by 101
Abstract
Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific [...] Read more.
Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific focus on implementing an efficient and non-intrusive edge-based solution. The methodology involves preprocessing motor current signals through fast Fourier transform (FFT) and Hilbert transform-based envelope analysis to extract harmonics without being masked by the fundamental supply frequency. These features are used to train machine learning models, considering variations in both speed and load. Experimental validation is conducted using the Paderborn University Bearing Dataset, demonstrating that the proposed approach achieves exceptional accuracy, precision, recall, and F1-score, exceeding 0.98 with models such as XGBoost, LightGBM, and CatBoost. While CatBoost exhibits the highest performance, LightGBM is selected as the optimal model due to its significantly reduced training time, making it well suited for edge computing applications. A comparison with prior studies confirms that the proposed method delivers competitive performance while utilizing fewer sensors, reducing hardware complexity. This research lays the groundwork for future predictive maintenance strategies ensuring real-time diagnostics and optimized industrial deployment. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
►▼ Show Figures

Figure 1

15 pages, 5477 KiB  
Article
Simulated Centrifugal Fan Blade Fault Diagnosis Based on Modulational Depthwise Convolution–One-Dimensional Convolution Neural Network (MDC-1DCNN) Model
by Zhaohui Ren, Yulin Liu, Tianzhuang Yu, Shihua Zhou, Yongchao Zhang and Zeyu Jiang
Machines 2025, 13(5), 356; https://doi.org/10.3390/machines13050356 - 24 Apr 2025
Viewed by 126
Abstract
Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex [...] Read more.
Existing intelligent fault diagnosis methods have been widely developed and proven to be effective in monitoring the operating status of key mechanical components. However, centrifugal fans, as important equipment in energy and manufacturing industries, have been used for a long time in complex and harsh environments such as boiler plants and gas turbines. Therefore, the vibration signals they generate show complex and diverse characteristics, which brings great challenges to the monitoring of centrifugal fan operation status. To solve this problem, this paper proposes a centrifugal fan blade fault diagnosis method based on a modulational depthwise convolution (DWconv)–one-dimensional convolution neural network (MDC-1DCNN). Specifically, firstly, a convolutional modulation module (CMM) with strong local perception and global modeling capability is designed by drawing on the Transformer self-attention mechanism and global context modeling idea. Second, multiple DWconv layers of different sizes are introduced to capture high-frequency shocks and low-frequency fluctuation information of different frequencies and durations in the signal. Next, a DWconv layer of size 11 is embedded in the multilayer perceptron to enhance spatial information representation while saving computational resources. Finally, to verify the effectiveness of the method, this paper simulates and analyzes the actual working state of centrifugal fan blades, constructs a simulation dataset, and builds a centrifugal fan experimental bench to obtain a real dataset. The experimental results show that the MDC-1DCNN framework significantly outperforms the existing methods in both simulation and experimental bench datasets, fully proving its versatility and effectiveness in centrifugal fan blade fault diagnosis. Full article
(This article belongs to the Section Machines Testing and Maintenance)
►▼ Show Figures

Figure 1

25 pages, 7036 KiB  
Article
Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction
by Lihua Shen, Baorui Du, He Fan and Hailong Yang
Machines 2025, 13(5), 355; https://doi.org/10.3390/machines13050355 - 24 Apr 2025
Viewed by 103
Abstract
To solve the problem of insufficient accuracy in tool wear process modeling and Remaining Useful Life (RUL) estimation, this study proposes a two-stage prediction method. Firstly, a linear prediction benchmark model is constructed: Support Vector Regression (SVR) is used to preliminarily model the [...] Read more.
To solve the problem of insufficient accuracy in tool wear process modeling and Remaining Useful Life (RUL) estimation, this study proposes a two-stage prediction method. Firstly, a linear prediction benchmark model is constructed: Support Vector Regression (SVR) is used to preliminarily model the tool wear process, obtaining initial prediction results and their error distribution. Building on this foundation, an Autoencoder (AE) is introduced to establish a nonlinear mapping relationship for the errors, achieving effective compensation of the SVR prediction results and establishing the SVR–AE prediction model. To further enhance model performance, the Ant Colony Optimization (ACO) algorithm is utilized to optimize three key parameters: the number of training epochs, batch size, and hidden layer dimensions, ultimately establishing the ACO–SVR–AE optimization model. Experimental validation demonstrates that on the PHM2010 dataset, compared to the Support Vector Regression (SVR) and Autoencoder (AE) models, the proposed method achieves average reductions of 26.1% in Mean Squared Error (MSE) and 14.5% in Mean Absolute Error (MAE). Compared to traditional random forest and neural network models, the MSE and MAE show average reductions of 32.3% and 25.3%. By combining linear modeling with nonlinear error compensation, this method provides an integrated optimization approach to prediction tasks in complex industrial scenarios. Full article
(This article belongs to the Section Advanced Manufacturing)
►▼ Show Figures

Figure 1

16 pages, 5241 KiB  
Article
Small Opening Flow Linearity Optimization of High-Pressure and Large-Flow Two-Way Proportional Throttle Valve
by Baosheng Wang, Liu Yang, Tianxiong Gao, Qingxin Meng, Shouwen Xiao and Chao Ai
Machines 2025, 13(5), 354; https://doi.org/10.3390/machines13050354 - 24 Apr 2025
Viewed by 121
Abstract
As a key component of the die-casting machine, the flow linearity of the high-pressure, large-flow proportional throttle valve is a critical factor affecting injection accuracy at low openings. However, under existing main valve structures, flow linearity is typically nonlinear. To address this issue, [...] Read more.
As a key component of the die-casting machine, the flow linearity of the high-pressure, large-flow proportional throttle valve is a critical factor affecting injection accuracy at low openings. However, under existing main valve structures, flow linearity is typically nonlinear. To address this issue, this paper introduces a main valve structure designed to enhance flow linearity at low openings. Firstly, the working principle of the proportional throttle valve is explained, and the relationship between flow rate and spool displacement is calculated. Secondly, a finite element model of the proportional throttle valve’s main valve flow field is developed. The impact of four primary spool structures on flow linearity is examined, and a small rounded rectangular throttle orifice is proposed. Thirdly, the size of the spool orifice is optimized using an orthogonal test method, and the trend of how different structures’ orifice sizes influence flow linearity is investigated. Finally, a valve prototype is fabricated, and the optimization’s effectiveness is experimentally verified. The results of the throttle valve’s design and optimization provide guidance for future work. Full article
(This article belongs to the Section Machine Design and Theory)
►▼ Show Figures

Figure 1

16 pages, 3791 KiB  
Article
Effect of Key Parameters on Ploughing Force Performance of Planing-Type Anti-Climbers
by Zhuyao Li, Jiyou Fei, Dongxue Song, Hong He, Chang Liu and Chong Zhang
Machines 2025, 13(5), 353; https://doi.org/10.3390/machines13050353 - 24 Apr 2025
Viewed by 115
Abstract
This paper proposes a mathematical model-based analytical approach to address the cutting force prediction and performance optimization challenges in planing-type anti-climbers for high-speed train passive safety systems. The method overcomes the reliance on experimental calibration inherent to conventional approaches, enabling the efficient quantitative [...] Read more.
This paper proposes a mathematical model-based analytical approach to address the cutting force prediction and performance optimization challenges in planing-type anti-climbers for high-speed train passive safety systems. The method overcomes the reliance on experimental calibration inherent to conventional approaches, enabling the efficient quantitative evaluation of anti-climber cutting performance. By equivalently modeling the collision energy dissipation process as an orthogonal cutting model, a theoretical framework integrating material dynamic response characteristics and impact boundary conditions was developed for direct cutting force prediction without experimental calibration. Finite element modeling implemented on the ABAQUS platform was employed for simulation analysis, supplemented by dynamic impact tests for validation. The results demonstrate that the model achieves ≤15% relative error compared with the simulation data and ≤5% deviation from the experimental measurements, confirming its engineering applicability. Sensitivity analysis reveals that cutting depth exhibits the most pronounced positive correlation with cutting force, while increased tool rake angle reduces cutting force. The dynamic equilibrium between thermal softening effects and strain rate strengthening leads to cutting force reduction with elevated cutting speed. This research establishes theoretical and technical foundations for the intelligent optimization of passive safety systems in rail transit equipment. Full article
(This article belongs to the Section Machine Design and Theory)
►▼ Show Figures

Figure 1

19 pages, 13911 KiB  
Article
Durability Comparison of SKD61 and FDAC Steel Mold Inserts in High-Pressure Die-Casting Process
by Hai Nguyen Le Dang, Van-Thuc Nguyen, Van Huong Hoang, Xuan Tien Vo and Van Thanh Tien Nguyen
Machines 2025, 13(5), 352; https://doi.org/10.3390/machines13050352 - 24 Apr 2025
Viewed by 154
Abstract
The high-pressure die-casting (HPDC) process involves injecting molten light metal into a steel mold under high pressure, resulting in parts with excellent surface quality and precise dimensions. However, this process subjects the mold to thermal fatigue and mechanical stress, which can lead to [...] Read more.
The high-pressure die-casting (HPDC) process involves injecting molten light metal into a steel mold under high pressure, resulting in parts with excellent surface quality and precise dimensions. However, this process subjects the mold to thermal fatigue and mechanical stress, which can lead to damage over time. This study investigated the wear characteristics of two types of inserts made from different steel materials, SKD61 steel and FDAC steel, under HPDC conditions. A thorough approach that combined computer simulations, experiments, and 3D scanning was employed to analyze wear patterns and dimensional changes after up to 300 casting cycles. The results indicate that the SKD61 steel outperformed the FDAC steel in terms of wear resistance and dimensional stability. The maximum deposition values of the SKD61 mold were only 0.009 mm, which was only 25% compared to the FDAC mold, indicating a significantly higher wear resistance. These findings are crucial for selecting and enhancing insert materials in HPDC, ultimately leading to higher-quality and more efficient casting. Full article
(This article belongs to the Section Advanced Manufacturing)
►▼ Show Figures

Figure 1

34 pages, 15651 KiB  
Article
Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi and Len Gelman
Machines 2025, 13(5), 351; https://doi.org/10.3390/machines13050351 - 23 Apr 2025
Viewed by 96
Abstract
Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults [...] Read more.
Rolling element bearings (REBs) are vital in rotating machinery, making fault detection essential for optimal performance and system reliability. This study assesses the effectiveness of a simple convolutional neural network (SCNN) and a transfer learning-based convolutional neural network (TL-CNN) for diagnosing REB faults using time-domain signals, frequency-domain spectra, and envelope frequency spectrum analysis. The study uses diverse datasets, including laboratory and industrial data under various operating conditions, covering fault types like inner race fault (IRF), outer race fault (ORF), rolling element fault (REF), and healthy (H) states. The main innovation is applying Transfer Learning (TL) with fine-tuning to improve model accuracy in identifying REB conditions by leveraging features learned from diverse datasets. An innovative algorithm is also introduced to identify resonance regions for optimal filter selection in envelope analysis, improving fault-related feature extraction and reducing noise. A preprocessing step that removes speed-related variations further enhances model accuracy by isolating fault features and minimizing the impact of rotational speed. The results show that transfer learning with fine-tuning, combined with the resonance region identification algorithm, significantly enhances fault detection accuracy. The TL-CNN model with envelope signal input achieves the highest accuracy across all scenarios, especially under variable operating conditions, and performs reliably on industrial data. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis)
►▼ Show Figures

Graphical abstract

15 pages, 1472 KiB  
Article
Intelligent Scheduling in Open-Pit Mining: A Multi-Agent System with Reinforcement Learning
by Gabriel Icarte-Ahumada and Otthein Herzog
Machines 2025, 13(5), 350; https://doi.org/10.3390/machines13050350 - 23 Apr 2025
Viewed by 118
Abstract
An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling [...] Read more.
An important process in the mining industry is material handling, where trucks are responsible for transporting materials extracted by shovels to different locations within the mine. The decision about the destination of a truck is very important to ensure an efficient material handling operation. Currently, this decision-making process is managed by centralized systems that apply dispatching criteria. However, this approach has the disadvantage of not providing accurate dispatching solutions due to the lack of awareness of potentially changing external conditions and the reliance on a central node. To address this issue, we previously developed a multi-agent system for truck dispatching (MAS-TD), where intelligent agents representing real-world equipment collaborate to generate schedules. Recently, we extended the MAS-TD (now MAS-TDRL) by incorporating learning capabilities and compared its performance with the original MAS-TD, which lacks learning capabilities. This comparison was made using simulated scenarios based on actual data from a Chilean open-pit mine. The results show that the MAS-TDRL generates more efficient schedules. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
►▼ Show Figures

Figure 1

28 pages, 18821 KiB  
Article
Determining the Range of Applicability of Analytical Methods for Belleville Springs and Novel Approach of Calculating Quasi-Progressive Spring Stacks
by Jędrzej Koralewski and Michał Wodtke
Machines 2025, 13(5), 349; https://doi.org/10.3390/machines13050349 - 23 Apr 2025
Viewed by 88
Abstract
This study investigates the accuracy of analytical methods for Belleville springs by comparing their results with finite element method (FEM) models to determine their applicability. Both well-established approaches, such as the Almen–Laszlo and Muhr–Niepage methods, and modern techniques, including Zheng’s energy method, Ferrari’s [...] Read more.
This study investigates the accuracy of analytical methods for Belleville springs by comparing their results with finite element method (FEM) models to determine their applicability. Both well-established approaches, such as the Almen–Laszlo and Muhr–Niepage methods, and modern techniques, including Zheng’s energy method, Ferrari’s method, and Leininger’s approach, were analyzed. The findings identified areas of consistency between analytical methods and FEM models, leading to the development of an algorithm for selecting the appropriate computational method for different types of Belleville springs. This research also examined the practical application ranges of Belleville springs, considering their structures and operating conditions, while assessing the effects of friction forces on individual springs and different stacks. Differences between hysteresis observed in FEM models and the results from analytical formulas were highlighted. A lack of analytical methods for determining the characteristics of spring assemblies with quasi-progressive behavior was identified, leading to the proposal of a novel algorithm for their calculation. The proposed method was validated using a specific example, confirming its accuracy. Future research directions were outlined to develop a universal computational approach for assemblies of Belleville springs with varying configurations and spring types. Full article
(This article belongs to the Section Machine Design and Theory)
►▼ Show Figures

Figure 1

17 pages, 2825 KiB  
Article
Performance Gain of Collaborative Versus Sequential Motion in Modular Robotic Manipulators for Pick-and-Place Operations
by Remy Carlier, Joris Gillis, Pieter De Clercq, Gianni Borghesan, Kurt Stockman and Jeroen D. M. De Kooning
Machines 2025, 13(5), 348; https://doi.org/10.3390/machines13050348 - 23 Apr 2025
Viewed by 176
Abstract
With the increasing demand for efficiency and profitability in industrial applications, modularity offers significant advantages such as system reconfiguration, reduced acquisition costs, and enhanced versatility. However, achieving compatibility across multi-vendor modular systems remains a challenge, particularly in motion control. This study focuses on [...] Read more.
With the increasing demand for efficiency and profitability in industrial applications, modularity offers significant advantages such as system reconfiguration, reduced acquisition costs, and enhanced versatility. However, achieving compatibility across multi-vendor modular systems remains a challenge, particularly in motion control. This study focuses on improving motion control and sensing compatibility and performance, partly using open-source tools to enhance performance in modular systems. In such systems, effective motion coordination between modules is crucial; without it, operations are constrained to sequential execution, limiting efficiency. This paper quantifies the performance benefits of collaborative motion compared to sequential motion in modular mechatronic systems for pick-and-place operations. The experimental validation, conducted on a robotic manipulator mounted on a linearly sliding platform, demonstrates a substantial improvement. The results show time savings of 36% to 52% and an approximate 35% reduction in energy consumption, highlighting the potential for improved productivity and sustainability in modular automation solutions. Full article
(This article belongs to the Special Issue Assessing New Trends in Sustainable and Smart Manufacturing)
►▼ Show Figures

Figure 1

22 pages, 46763 KiB  
Article
MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery
by Bo Wang and Shuai Zhao
Machines 2025, 13(5), 347; https://doi.org/10.3390/machines13050347 - 23 Apr 2025
Viewed by 170
Abstract
Deep learning (DL)-based methods have shown great success in multi-category fault diagnosis due to their hierarchical networks and automatic feature extraction. However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated engineering environments. In this paper, [...] Read more.
Deep learning (DL)-based methods have shown great success in multi-category fault diagnosis due to their hierarchical networks and automatic feature extraction. However, their superior performance is mostly based on single-task learning, which makes them unsuitable for increasingly sophisticated engineering environments. In this paper, a novel multi-task graph-guided convolutional network with an attention mechanism for intelligent fault diagnosis, named MTAGCN, is proposed. Most existing fault diagnosis models are commonly bounded by a single diagnosis objective, especially when handling multiple tasks jointly. To address this limitation, a new multi-task fault diagnosis framework is designed, incorporating an attention mechanism between the task-specific module and task-shared modules. This framework enables multiple related tasks to be learned jointly while improving diagnostic and identification performance. Moreover, it is observed that most existing DL-based methods share incomplete fault representations, leading to unsatisfactory fault diagnosis. To overcome this issue, a graph convolutional network (GCN)-based fault diagnosis framework is introduced, which not only captures structural characteristics but also enhances diagnostic effectiveness. Comprehensive experiments based on three case studies demonstrate that the proposed MTAGCN outperforms state-of-the-art (SOTA) methods, striking a good balance between accuracy and multi-task learning. Full article
(This article belongs to the Special Issue Signal Processing and Artificial Intelligence Technology for High-End Equipment Fault Diagnosis)
►▼ Show Figures

Figure 1

20 pages, 5784 KiB  
Article
Lower Limb Motion Recognition Based on Surface Electromyography Decoding Using S-Transform Energy Concentration
by Baoyu Li, Guanghua Xu, Jinju Pei, Dan Luo, Hui Li, Chenghang Du, Kai Zhang and Sicong Zhang
Machines 2025, 13(5), 346; https://doi.org/10.3390/machines13050346 - 23 Apr 2025
Viewed by 164
Abstract
Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, [...] Read more.
Lower limb motion recognition using surface electromyography (EMG) enhances human-computer interaction for intelligent prostheses. This study proposes a surface electromyography (EMG)-based scheme for lower limb motion recognition to enhance human-computer interaction in intelligent prostheses. Addressing the loss of phase information in existing methods, the approach combines S-transform energy concentration and multi-channel fusion analysis. EMG signals from six lower limb muscles of 10 subjects performing four movements (level walk, stair ascent, stair descent, and obstacle crossing) were analyzed. Correlation analysis identified the most relevant and least correlated muscles, optimizing signal quality. Using support vector machines (SVM), motion recognition accuracy was evaluated for single-channel and multi-channel signals. Results indicated that the semi-tendon and rectus femoris muscles achieved 80.71% accuracy with simple time-frequency features, while the medial gastrocnemius and rectus femoris reached 93.70% accuracy with S-transform energy concentration. Multi-channel fusion (rectus femoris, biceps femoris, and medial gastrocnemius) based on S-transform achieved over 96% accuracy, demonstrating superior recognition performance and potential for improving adaptive human-robot interaction in prosthetic control. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
►▼ Show Figures

Figure 1

16 pages, 3060 KiB  
Article
Influence of Excitation Disturbances on Oscillation of a Belt System with Collisions
by Marek Lampart and Jaroslav Zapoměl
Machines 2025, 13(5), 345; https://doi.org/10.3390/machines13050345 - 23 Apr 2025
Viewed by 138
Abstract
In addition to technological influences, real-world belt and conveyor systems must contend with loading effects characterized primarily by randomness. Evaluating the impact of these effects on system behavior involves the creation of a computational model. In this innovative approach, disturbances are expressed by [...] Read more.
In addition to technological influences, real-world belt and conveyor systems must contend with loading effects characterized primarily by randomness. Evaluating the impact of these effects on system behavior involves the creation of a computational model. In this innovative approach, disturbances are expressed by discretization and round-off errors arising throughout the solution of the controlling equations. Simulations conducted under this model demonstrate that these disturbances have the potential to generate hidden and co-existing attractors. Additionally, they have the potential to initiate shifts between oscillations of varying periods or transitions from regular to chaotic motions. This exploration sheds light on the intricate dynamics and behaviors exhibited by belt and conveyor systems in the face of various disturbances. Full article
(This article belongs to the Special Issue Nonlinear Dynamics, Vibration Monitoring and Fault Diagnostics in Rotating Systems)
►▼ Show Figures

Figure 1

25 pages, 6005 KiB  
Article
Simplified Data-Driven Models for Gas Turbine Diagnostics
by Igor Loboda, Juan Luis Pérez Ruíz, Iván González Castillo, Jonatán Mario Cuéllar Arias and Sergiy Yepifanov
Machines 2025, 13(5), 344; https://doi.org/10.3390/machines13050344 - 22 Apr 2025
Viewed by 172
Abstract
The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second [...] Read more.
The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second approach relies on data-driven models, makes diagnosis in the space of measurement deviations, and involves pattern recognition techniques. Although a thermodynamic model is an essential element of GPD, it has limitations. This model is a complex software critical to computer resources, and the computation sometimes does not converge. Therefore, it is difficult to use the model in online applications. Since the 1990s, we have developed many thermodynamic models for different engines. Since the 2000s, simplified data-driven models were investigated. This paper proposes to substitute a thermodynamic model for novel simplified data-driven models that have the same functionality, i.e., take into consideration the influence of both operating conditions and engine faults. The proposed models are formed and compared with the underlying thermodynamic model. To obtain a solid conclusion about these models, they are verified in twelve test cases formed by three test-case engines, two model types, and two approximation functions. Although the accuracy of the simplified models varies from 1.15% to 0.0082%, it was found acceptable even for the worst case. Thus, these simple-but-accurate models with the functionality of a physics-based model represent a good replacement for the latter. It is expected that the models will stimulate the further development of advanced diagnostic systems. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-35
Previous Issue
Back to TopTop