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Machines, Volume 11, Issue 2 (February 2023) – 192 articles

Cover Story (view full-size image): An algorithm to predict the remaining useful life (RUL) of aircraft engines using the recently published NASA CMAPPS datasets has been developed. The algorithm comprises feature selection using a hybrid metric, the Logistic Regression for health index estimation, and the Unscented Kalman Filter (UKF) for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison, and dataset 03 is employed as a new state-of-the-art dataset. As a result, the developed algorithm yields 34.5–55.6% better performance in terms of the Root Mean Squared Error (RMSE) compared with the previous works. More importantly, the proposed method is explainable and transparent. In addition, it quantifies the uncertainty during the prediction process. View this paper
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19 pages, 3693 KiB  
Article
Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm
by Minan Tang, Yaguang Yan, Bo An, Wenjuan Wang and Yaqi Zhang
Machines 2023, 11(2), 316; https://doi.org/10.3390/machines11020316 - 20 Feb 2023
Cited by 5 | Viewed by 2398
Abstract
Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming [...] Read more.
Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy. Full article
(This article belongs to the Topic Intelligent Systems and Robotics)
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27 pages, 8769 KiB  
Article
Camera-Based Local and Global Target Detection, Tracking, and Localization Techniques for UAVs
by Ioannis Daramouskas, Dimitrios Meimetis, Niki Patrinopoulou, Vaios Lappas, Vassilios Kostopoulos and Vaggelis Kapoulas
Machines 2023, 11(2), 315; https://doi.org/10.3390/machines11020315 - 20 Feb 2023
Cited by 4 | Viewed by 3198
Abstract
Multiple-object detection, localization, and tracking are desirable in many areas and applications, as the field of deep learning has developed and has drawn the attention of academics in computer vision, having a plethora of networks now achieving excellent accuracy in detecting multiple objects [...] Read more.
Multiple-object detection, localization, and tracking are desirable in many areas and applications, as the field of deep learning has developed and has drawn the attention of academics in computer vision, having a plethora of networks now achieving excellent accuracy in detecting multiple objects in an image. Tracking and localizing objects still remain difficult processes which require significant effort. This work describes an optical camera-based target detection, tracking, and localization solution for Unmanned Aerial Vehicles (UAVs). Based on the well-known network YOLOv4, a custom object detection model was developed and its performance was compared to YOLOv4-Tiny, YOLOv4-608, and YOLOv7-Tiny. The target tracking algorithm we use is based on Deep SORT, providing cutting-edge tracking. The proposed localization approach can accurately determine the position of ground targets identified by the custom object detection model. Moreover, an implementation of a global tracker using localization information from up to four UAV cameras at a time. Finally, a guiding approach is described, which is responsible for providing real-time movement commands for the UAV to follow and cover a designated target. The complete system was evaluated in Gazebo with up to four UAVs utilizing Software-In-The-Loop (SITL) simulation. Full article
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20 pages, 7984 KiB  
Article
Design and Experiments of a Two-Stage Fuzzy Controller for the Off-Center Steer-by-Wire System of an Agricultural Mobile Robot
by Jiwei Qu, Zhe Zhang, Hongji Li, Ming Li, Xiaobo Xi and Ruihong Zhang
Machines 2023, 11(2), 314; https://doi.org/10.3390/machines11020314 - 20 Feb 2023
Cited by 1 | Viewed by 1530
Abstract
This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering [...] Read more.
This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering controller for the off-center steer-by-wire system of a robot. The influence of the PWM duty cycle on the steering motion under different conditions is firstly tested on a test bench. Based on the optimal duty cycles of different cases found in the test, a two-stage fuzzy controller of the duty cycle is designed for the steering system. The first stage of the controller is used to dynamically adjust the PWM duty cycle of the electromagnetic friction lock (EFL). The second stage is designed to realize the self-tuning of the fuzzy controller’s quantization factor and the scale factor. Through two-stage control, the motion of the in-wheel motor and the EFL can be coordinated to realize stable and rapid steering. Considering the robots’ primary application in field roads at present, road tests were ultimately conducted to verify the proposed method. The test results show that the angle response rate of the steering arm is elevated with the increase in the steering angle signal. The proposed controller can sensitively track the target angles with smaller overshoot, yaw rate and lateral acceleration, and better steering accuracy than the PID (proportional–integral–differential) controller under different working conditions. Full article
(This article belongs to the Special Issue Design and Control of Mobile Robots)
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22 pages, 8481 KiB  
Article
Research and Analysis of the Characteristics of the Brushless Doubly-Fed Machine with High-Performance Decoupling Control
by Chaoying Xia and Nannan Wang
Machines 2023, 11(2), 313; https://doi.org/10.3390/machines11020313 - 20 Feb 2023
Cited by 2 | Viewed by 1393
Abstract
The paper presents the state-space (SS) model of the brushless double-fed machine (BDFM) by taking the negative conjugate (NC) transformation of the power machine’s correlation variable when the current source of the control machine is supplied in the m-t reference frame. Based on [...] Read more.
The paper presents the state-space (SS) model of the brushless double-fed machine (BDFM) by taking the negative conjugate (NC) transformation of the power machine’s correlation variable when the current source of the control machine is supplied in the m-t reference frame. Based on this, the testing method of machine parameters is given, and the SS model described by five parameters is obtained. The derivation process and realization method of the slip-frequency vector feedback linearization control (SFV-FLC) strategy are given. Then, researching the SS equation in the m-t reference frame by the control machine rotor field-oriented, the relationship between the maximum and minimum output torque and the control machine rotor flux amplitude and slip velocity, the machine parameters are given considering power supply constraints. Finally, the validity of the theoretical analysis is verified by simulation and experiment, and the feasibility of the decoupling control method are also demonstrated. Full article
(This article belongs to the Special Issue Advanced Data Analytics in Intelligent Industry: Theory and Practice)
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26 pages, 8043 KiB  
Article
Gear Crack Detection Based on Vibration Analysis Techniques and Statistical Process Control Charts (SPCC)
by Rasheed Majeed Jorani, Maroua Haddar, Fakher Chaari and Mohamed Haddar
Machines 2023, 11(2), 312; https://doi.org/10.3390/machines11020312 - 20 Feb 2023
Cited by 5 | Viewed by 1941
Abstract
Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes to apply a new methodology using time-domain analysis, frequency-domain analysis, and statistical process control charts (SPCC) for gear crack detection of [...] Read more.
Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes to apply a new methodology using time-domain analysis, frequency-domain analysis, and statistical process control charts (SPCC) for gear crack detection of a 10 DOF dynamic model of spiral bevel gear system (SBGS). The gear mesh stiffness effect used in the model has been studied analytically for different levels of crack faults. Adding Gaussian white noise is discussed as the first step to simulating the initial modeling signals of real-world conditions. Second, time-domain signal analysis was performed to identify periodic vibration pulses as failure components and calculate the statistical standard deviation (STD) feature as a fault-sensitive feature. Third, a fast Fourier transform (FFT) to time signals of the variable gear mesh stiffness was applied to determine the gear mesh frequency and sidebands to detect tooth cracks. Fourth, the SPCC was designed using the Shewhart X-bar chart and an exponentially weighted moving average (EWMA) chart based on the STD feature of the healthy gears. Finally, in the testing stage, the control charts are carried out with simulation signals under faulty conditions to detect the different levels of cracks. The results showed that the EWMA chart outperformed the time domain analysis, frequency domain analysis, and Shewhart X-bar chart in detecting all levels of cracks at an early stage. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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16 pages, 7118 KiB  
Article
Typical Fault Modeling and Vibration Characteristics of the Turbocharger Rotor System
by Jiahao Wang, Huabing Wen, Haiyu Qian, Junhua Guo, Junchao Zhu, Jiwei Dong and Hua Shen
Machines 2023, 11(2), 311; https://doi.org/10.3390/machines11020311 - 20 Feb 2023
Cited by 2 | Viewed by 1352
Abstract
To study the typical failure mechanism (rotor unbalance, rotor friction, and rotor crack) and vibration characteristics of the turbocharger rotor system, a rotor system dynamics simulation model was established by an improved four-node aggregate parameter method. The geometric and physical characteristics of the [...] Read more.
To study the typical failure mechanism (rotor unbalance, rotor friction, and rotor crack) and vibration characteristics of the turbocharger rotor system, a rotor system dynamics simulation model was established by an improved four-node aggregate parameter method. The geometric and physical characteristics of the rotor system under three failure states and its dynamics under operation were analyzed. Thus, a typical failure dynamics simulation model of the rotor system was established. On this basis, the output failure simulation signal was extracted using the Hu invariant moment feature extraction method to analyze the system vibration characteristics under each typical failure state of the rotor system. The results show that the model in this paper can effectively reduce the computational volume and computational time, and the errors of numerical simulation were less than 3%. When an unbalance fault occurred in the rotor system, the shaft trajectory was “0” shaped and the response spectrum was dominated by 1X. When the rotor system was frictional, the shaft trajectory was a slightly concave “8” shape, and the response spectrum was dominated by 0.5X. When the rotor system was cracked, the axial trajectory was a “vortex”, and the response spectrum was dominated by 0.5X. Thus, the study of typical failure mechanism and vibration characteristics of a turbocharger rotor system by simulation calculation is effective and has good research prospects, providing an important technical reference for dynamic analysis and fault diagnosis of the rotor system. Full article
(This article belongs to the Special Issue Rotor Dynamics and Rotating Machinery)
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20 pages, 3459 KiB  
Article
A Hybrid Fuzzy Multi-Criteria Decision-Making Model for Evaluating the Influence of Industry 4.0 Technologies on Manufacturing Strategies
by Fawaz M. Abdullah, Abdulrahman M. Al-Ahmari and Saqib Anwar
Machines 2023, 11(2), 310; https://doi.org/10.3390/machines11020310 - 20 Feb 2023
Cited by 3 | Viewed by 2149
Abstract
Manufacturing is transitioning from traditional and mass manufacturing to mass personalization, fast, and intelligent production. Through full automation in various fields and data sharing, Industry 4.0 (I4.0) contributes to the digitalization of manufacturing by enhancing industrial flexibility and product customization. I4.0 is being [...] Read more.
Manufacturing is transitioning from traditional and mass manufacturing to mass personalization, fast, and intelligent production. Through full automation in various fields and data sharing, Industry 4.0 (I4.0) contributes to the digitalization of manufacturing by enhancing industrial flexibility and product customization. I4.0 is being utilized as a strategy for advanced manufacturing to counter global competitiveness. A company’s manufacturing strategy outputs (MSOs) are critical to its ability to move forward and remain competitive. Despite their importance, I4.0 technologies have received less attention in the literature, and it is unclear how they influence MSOs. Thus, this study aims to build a powerful hybrid MCDM method for ranking the influence of I4.0 technologies on MSOs by adopting a combination of AHP and fuzzy TOPSIS. The application of fuzzy set theory has addressed the ambiguity in comparing various I4.0 technologies. The AHP was used to calculate the weights of criteria and sub-criteria, and the fuzzy-TOPSIS method was utilized to rank the I4.0 technologies. The results revealed that the cost criterion is the most critical factor when implementing I4.0 technologies. In contrast, additive manufacturing (AM) is the most suitable I4.0 technology for countering global competition. Full article
(This article belongs to the Special Issue Advances in Smart Manufacturing and Industry 4.0)
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25 pages, 11680 KiB  
Article
Research Regarding the Development of the Combustion Chamber of Internal Combustion Engines with Opposite Pistons
by Horia Beles, Adrian Tusinean, Tudor Mitran and Florin Bogdan Scurt
Machines 2023, 11(2), 309; https://doi.org/10.3390/machines11020309 - 20 Feb 2023
Cited by 1 | Viewed by 2403
Abstract
The reduction in environment pollutant emissions is one of the main challenges regarding ground transportation. Internal combustion engines, used especially in hybrid propulsion systems, may be a solution in the transition to fully electric cars. Therefore, more efficient engines in terms of fuel [...] Read more.
The reduction in environment pollutant emissions is one of the main challenges regarding ground transportation. Internal combustion engines, used especially in hybrid propulsion systems, may be a solution in the transition to fully electric cars. Therefore, more efficient engines in terms of fuel consumption, emission generation and power density must be developed. This paper presents research regarding the architecture of the combustion chamber of an internal combustion engine with opposed pistons. The aim of this research was to find a combustion chamber architecture that would enable the engine to perform close to the program target of: NOx < 3.5 g/kWh, smoke (FSN) < 1, specific fuel consumption (bsfc) < 198 g/kWh. Three variants of the combustion chamber’s architecture have been studied. After the experimental research, the conclusion was that none of them fully reached the target; however, significant improvements have been achieved compared with the starting point. As a result, further research needs to be carried out in order to reach and even exceed the target. Full article
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16 pages, 4411 KiB  
Article
Hardware-in-the-Loop Implementation of ROMAtrix, a Smart Transformer for Future Power Grids
by Amir Ostadrahimi and Stefano Bifaretti
Machines 2023, 11(2), 308; https://doi.org/10.3390/machines11020308 - 19 Feb 2023
Cited by 1 | Viewed by 1579
Abstract
The evolution of power generation brings about extensive changes in other parts of the grid, especially in the transmission and distribution components. Within the scope of the Internet of Energy (IoE), electric power flows more flexibly between different parts of the grid. DC [...] Read more.
The evolution of power generation brings about extensive changes in other parts of the grid, especially in the transmission and distribution components. Within the scope of the Internet of Energy (IoE), electric power flows more flexibly between different parts of the grid. DC power will play an essential role in IoE. Decentralized photovoltaic panels, energy storage, electric vehicle charging stations, and data centers are some of the significant components of future grids dealing with DC power. As a result, power transformers must be appropriately modified to manage power among the different parts of the grid. A power electronic transformer (PET), also known as a solid-state transformer (SST) or smart transformer (ST), is a solution enabling a power grid to deal with this growing complexity. ROMAtrix, as a matrix-converter-based ST, is a developing project targeting future power grids. ROMAtrix realizes the application of a medium voltage (MV) transformer using commercially available power electronic semiconductors. Due to the distinctive features of ROMAtrix and a high number of switches, the implementation of the control system using a single control board is highly demanding. This paper aims to illustrate the implementation, on a field-programmable gate array (FPGA), of pulse width modulation (SVMPWM) applied to the ROMAtrix, considering specific switching patterns. The proposed switching procedure was simulated with PLECS and validated with the hardware-in-the-loop using the OPAL-RT solver. Full article
(This article belongs to the Special Issue Advances in High-Power Converters)
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24 pages, 5597 KiB  
Article
Euler Representation-Based Structural Balance Discriminant Projection for Machinery Fault Diagnosis
by Maoyan Zhang, Yanmin Zhu, Shuzhi Su, Xianjin Fang and Ting Wang
Machines 2023, 11(2), 307; https://doi.org/10.3390/machines11020307 - 19 Feb 2023
Cited by 1 | Viewed by 1139
Abstract
Fault diagnosis methods are usually sensitive to outliers and it is difficult to obtain and balance global and local discriminant information, which leads to poor separation between classes of low-dimensional discriminant features. For this problem, we propose an Euler representation-based structural balance discriminant [...] Read more.
Fault diagnosis methods are usually sensitive to outliers and it is difficult to obtain and balance global and local discriminant information, which leads to poor separation between classes of low-dimensional discriminant features. For this problem, we propose an Euler representation-based structural balance discriminant projection (ESBDP) algorithm for rotating machine fault diagnosis. First, the method maps the high-dimensional fault features into the Euler representation space through the cosine metric to expand the differences between heterogeneous fault samples while reducing the impact on outliers. Then, four objective functions with different structure and class information are constructed in this space. On the basis of fully mining the geometric structure information of fault data, the local intra-class aggregation and global inter-class separability of the low-dimensional discriminative features are further improved. Finally, we provide an adaptive balance strategy for constructing a unified optimization model of ESBDP, which achieves the elastic balance between global and local features in the projection subspace. The diagnosis performance of the ESBDP algorithm is explored by two machinery fault cases of bearing and gearbox. Encouraging experimental results show that the algorithm can capture effective fault discriminative features and can improve the accuracy of fault diagnosis. Full article
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17 pages, 7398 KiB  
Article
Performance Comparison of Two Architectures of 6R Articulated Robots
by Giovanni Boschetti and Teresa Sinico
Machines 2023, 11(2), 306; https://doi.org/10.3390/machines11020306 - 18 Feb 2023
Viewed by 2132
Abstract
This paper presents a comparison between two different 6R articulated robot architectures, one with a spherical wrist and the other with a non-spherical wrist and three consecutive parallel axes, which are found mainly in collaborative arms. The performance of the two architectures has [...] Read more.
This paper presents a comparison between two different 6R articulated robot architectures, one with a spherical wrist and the other with a non-spherical wrist and three consecutive parallel axes, which are found mainly in collaborative arms. The performance of the two architectures has been evaluated in terms of linear and rotational velocity using the Kinematic Directional Index (KDI). The results highlight the relation between the robot’s velocity along a direction and the joint velocities. In this way, the proposed approach allows the evaluation of the best performance in a direction and the joints that limit the considered motion. Full article
(This article belongs to the Special Issue Design and Control of Industrial Robots)
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21 pages, 11410 KiB  
Article
Effects of Tooth Modification in the Involute Helical Gear Form-Grinding Process on Loaded Transmission Character with Consideration of Tooth Axial Inclination Error
by Yongming Yang, Yunlong Wu, Yan Li and Xinrong Liu
Machines 2023, 11(2), 305; https://doi.org/10.3390/machines11020305 - 17 Feb 2023
Cited by 3 | Viewed by 1927
Abstract
Due to the existence of machining and installation errors, axis parallelism error of gear pairs occurs, which causes eccentric load and mesh in-out impact, thus weakening loaded transmission character. To solve this problem, the axis parallelism error of gear pairs was equated with [...] Read more.
Due to the existence of machining and installation errors, axis parallelism error of gear pairs occurs, which causes eccentric load and mesh in-out impact, thus weakening loaded transmission character. To solve this problem, the axis parallelism error of gear pairs was equated with tooth axial inclination error based on the gear-meshing principle. On this basis, we established the tooth modification model with tooth axial inclination error as the variable according to involute helical gear form-grinding process. Then, the degradation of loaded transmission character caused by axis parallelism error of gear pairs was quantitatively analyzed. The gear grinding, gear measuring, and gearbox vibration measuring were, respectively, performed on high-precision CNC horizontal gear form-grinding machine tool L300G, Gleason 350 GMS, and JWY-II multifunctional gearbox loading test bench. The results show that the proposed method can effectively reduce eccentric load and mesh in-out impact and significantly improve loaded transmission character. Therefore, it can provide a theoretical and experimental basis for the research of high-performance gear-grinding technology of gear-grinder machines. Full article
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21 pages, 5592 KiB  
Article
Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains
by Jiujian Wang, Shaopu Yang, Yongqiang Liu and Guilin Wen
Machines 2023, 11(2), 304; https://doi.org/10.3390/machines11020304 - 17 Feb 2023
Cited by 1 | Viewed by 1502
Abstract
High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to [...] Read more.
High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness. Full article
(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)
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19 pages, 11666 KiB  
Article
An Integrated Obstacle Avoidance Controller Based on Scene-Adaptive Safety Envelopes
by Kang Li, Zhishuai Yin, Yuanxin Ba, Yue Yang, Yuanhao Kuang and Erqian Sun
Machines 2023, 11(2), 303; https://doi.org/10.3390/machines11020303 - 17 Feb 2023
Cited by 1 | Viewed by 1690
Abstract
This paper presents an integrated active obstacle avoidance controller in the Model Predictive Control (MPC) framework to ensure adaptive collision-free obstacle avoidance under complex scenarios while maintaining a good level of vehicle stability and steering smoothness. Firstly, with the observed road conditions and [...] Read more.
This paper presents an integrated active obstacle avoidance controller in the Model Predictive Control (MPC) framework to ensure adaptive collision-free obstacle avoidance under complex scenarios while maintaining a good level of vehicle stability and steering smoothness. Firstly, with the observed road conditions and obstacle states as inputs, a data-driven Gaussian Process Regression (GPR) model is constructed and trained to generate confidence intervals, as scene-adaptive dynamic safety envelopes represent the safety boundaries of obstacle avoidance. Subsequently, the generated safety envelopes are transformed into soft and hard constraints, incorporated into the MPC controller and rolling updated in the prediction horizon to further cope with uncertain and rapidly evolving driving conditions. Minimizing both the control increments and stability feature parameters are formulated into the objectives of the MPC controller. By solving the multi-objective optimization problem with soft and hard constraints imposed, control commands are obtained to steer the vehicle in order to avoid the obstacles safely and smoothly with guaranteed vehicle stability. The experiments conducted on a motion-base driving simulator show that the proposed controller manages to perform safe and stable obstacle avoidance even under hazardous conditions. It is also verified that the proposed controller can be applied to more complex scenarios with dynamic obstacles presented. Full article
(This article belongs to the Special Issue Advanced Modeling, Analysis and Control for Electrified Vehicles)
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24 pages, 5324 KiB  
Article
Fingerprinting-Based Indoor Positioning Using Data Fusion of Different Radiocommunication-Based Technologies
by Dominik Csik, Ákos Odry and Peter Sarcevic
Machines 2023, 11(2), 302; https://doi.org/10.3390/machines11020302 - 17 Feb 2023
Cited by 7 | Viewed by 1729
Abstract
Wireless-radio-communication-based devices are used in more and more places with the spread of Industry 4.0. Localization plays a crucial part in many of these applications. In this paper, a novel radiocommunication-based indoor positioning method is proposed, which applies the fusion of fingerprints extracted [...] Read more.
Wireless-radio-communication-based devices are used in more and more places with the spread of Industry 4.0. Localization plays a crucial part in many of these applications. In this paper, a novel radiocommunication-based indoor positioning method is proposed, which applies the fusion of fingerprints extracted with various technologies to improve the overall efficiency. The aim of the research is to apply the differences, which occur due to that different technologies behave differently in an indoor space. The proposed method was validated using training and test data collected in a laboratory. Four different technologies, namely WiFi received signal strength indication (RSSI), ultra-wideband (UWB) RSSI, UWB time of flight (TOF) and RSSI in 433 MHz frequency band and all of their possible combinations, were tested to examine the performance of the proposed method. Three widely used fingerprinting algorithms, the weighted k-nearest neighbor, the random forest, and the artificial neural network were implemented to evaluate their efficiency with the proposed method. The achieved results show that the accuracy of the localization can be improved by combining different technologies. The combination of the two low-cost technologies, i.e., the WiFi and the 433 MHz technology, resulted in an 11% improvement compared to the more accurate technology, i.e., the 433 MHz technology. Combining the UWB module with other technologies results in a less significant improvement since this sensor provides lower error rates, when used alone. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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15 pages, 8056 KiB  
Article
Design of Spoke-Type Permanent Magnet Synchronous Generator for Low Capacity Wind Turbine Considering Magnetization and Cogging Torques
by Dong-Ho Kim, Hyun-Jo Pyo, Won-Ho Kim, Ju Lee and Ki-Doek Lee
Machines 2023, 11(2), 301; https://doi.org/10.3390/machines11020301 - 17 Feb 2023
Cited by 2 | Viewed by 2016
Abstract
Permanent magnet synchronous generators (PMSGs) with high output density per unit volume are becoming widespread in wind-power generation systems. Among them, spoke-type PMSGs are more challenging to magnetize than other PMSGs, owing to their structural characteristics. Magnetization performance is critical because it is [...] Read more.
Permanent magnet synchronous generators (PMSGs) with high output density per unit volume are becoming widespread in wind-power generation systems. Among them, spoke-type PMSGs are more challenging to magnetize than other PMSGs, owing to their structural characteristics. Magnetization performance is critical because it is directly related to the demagnetization and mass productivity of permanent magnets, and load performance is reduced when non-magnetization occurs due to the low magnetization performance. Additionally, the starting performance is crucial in wind turbines and is influenced by the cogging torque of the PMSG. This is because starting a wind turbine with a large cogging torque is more challenging. Therefore, this study proposes a spoke-type PMSG rotor shape design for low capacity wind turbines that considers magnetization and cogging torques. We analyzed the principle of magnetization and the factors influencing magnetization performance, and designed a rotor shape with improved magnetization performance. Additionally, we applied an asymmetric rotor barrier structure to reduce the cogging torque and analyze the performance of the final model using finite element analysis. We analyzed the temperature saturation during the operation of the final model using a thermal network method and validated the irreversible demagnetization accordingly. Full article
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21 pages, 7233 KiB  
Article
Intelligent-PID with PD Feedforward Trajectory Tracking Control of an Autonomous Underwater Vehicle
by Zafer Bingul and Kursad Gul
Machines 2023, 11(2), 300; https://doi.org/10.3390/machines11020300 - 17 Feb 2023
Cited by 21 | Viewed by 4258
Abstract
This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is [...] Read more.
This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is presented to guarantee stable and precise trajectory tracking performance in the complex underwater environment for AUVs. In the proposed hybrid controller, intelligent-PID (i-PID) and PD feedforward controllers are combined to achieve better disturbance rejections and initial tracking error compensations while keeping the trajectory tracking precision. A mathematical model of an AUV is derived, and ocean current dynamics are included to obtain better fidelity when examining ocean current effects. In order to evaluate the trajectory tracking control performance of the proposed controller, computer simulations are conducted on the LIVA AUV with a compelling trajectory under various disturbances. The results are compared with the two degrees-of-freedom (DOF) i-PID, i-PID, and PID controllers to examine control performance improvements with the guaranteed trajectory tracking stability. The comparative results revealed that the i-PID with PD feedforward controller provides an effective trajectory tracking control performance and excellent disturbance rejections for the entire trajectory of the AUV. Full article
(This article belongs to the Special Issue Control and Mechanical System Engineering)
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13 pages, 619 KiB  
Communication
Distributed Predefined-Time Optimization for Second-Order Systems under Detail-Balanced Graphs
by Pablo De Villeros, Juan Diego Sánchez-Torres, Aldo Jonathan Muñoz-Vázquez, Michael Defoort, Guillermo Fernández-Anaya and Alexander Loukianov
Machines 2023, 11(2), 299; https://doi.org/10.3390/machines11020299 - 17 Feb 2023
Cited by 3 | Viewed by 1488
Abstract
This paper studies the problem of distributed predefined-time optimization for leaderless consensus of second-order multi-agent systems under a class of weighted digraphs. The proposed framework has two main steps. In the first step, the agents communicate to perform a consensus-based distributed predefined-time optimization [...] Read more.
This paper studies the problem of distributed predefined-time optimization for leaderless consensus of second-order multi-agent systems under a class of weighted digraphs. The proposed framework has two main steps. In the first step, the agents communicate to perform a consensus-based distributed predefined-time optimization and to generate a constant optimal output reference for each agent. In the second step, each agent tracks its corresponding optimal output reference, using a sliding-mode controller to reach the global optimum in a predefined time, even under matched disturbances. The proposed algorithm relies explicitly on user-defined constant parameters. Numerical simulations are performed to validate the efficacy of the algorithm. Full article
(This article belongs to the Special Issue Advanced Motion Control of Multiple Robots)
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20 pages, 8665 KiB  
Article
Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study
by Panagiotis Stavropoulos, Alexios Papacharalampopoulos and Kyriakos Sabatakakis
Machines 2023, 11(2), 298; https://doi.org/10.3390/machines11020298 - 17 Feb 2023
Cited by 6 | Viewed by 1378
Abstract
For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond [...] Read more.
For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system. Full article
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13 pages, 4490 KiB  
Article
ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction
by Renwang Li, Xiaolei Ye, Fangqing Yang and Ke-Lin Du
Machines 2023, 11(2), 297; https://doi.org/10.3390/machines11020297 - 16 Feb 2023
Cited by 5 | Viewed by 2120
Abstract
In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids [...] Read more.
In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual feature extraction. Then the temporal relationship learning between multidimensional feature vectors is performed by introducing a long short-term memory (LSTM) network to make up for the lack of long-short distance dependence of the captured sequence of the CNN network. Finally, an attention mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then performs as a tool wear predictor. The model is compared with several state-of-the-art models on the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction accuracy, but with similar computational complexity. Full article
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19 pages, 5908 KiB  
Article
Thermal-Fluid-Solid Coupling Simulation and Oil Groove Structure Optimization of Wet Friction Clutch for High-Speed Helicopter
by Wuzhong Tan, Zhi Chen, Zhizuo Li and Hongzhi Yan
Machines 2023, 11(2), 296; https://doi.org/10.3390/machines11020296 - 16 Feb 2023
Cited by 3 | Viewed by 1871
Abstract
Wet friction clutch is the key functional component of the high-speed helicopter variable-speed transmission system, which is used to change the power transmission path. In the engagement process of wet friction clutch, the driving/driven disc will produce drag torque under the shearing of [...] Read more.
Wet friction clutch is the key functional component of the high-speed helicopter variable-speed transmission system, which is used to change the power transmission path. In the engagement process of wet friction clutch, the driving/driven disc will produce drag torque under the shearing of lubricating oil, which reduces the transmission efficiency. This unnecessary drag torque reduces efficiency and increases clutch temperature. The temperature increase promotes the wear of gears and bearings and the aging deformation of friction plates, which leads to local wear and reduces the service life of the clutch. From the principle of wet friction clutch, the oil groove structure is directly related to the drag torque and the temperature rise of friction disc. It is very important for the long-distance flight and service life of high-speed helicopters to obtain the groove structure with low drag torque and low temperature rise. In order to solve this problem, taking the wet friction clutch of a high-speed helicopter as the research object, based on the radial and annular compound groove, the thermal-fluid-solid coupling simulation model of the wet friction clutch is established to obtained the flow characteristics and temperature field distribution of the lubricating oil in the friction disc oil groove, and to analyze the influence law of the oil groove structure parameters on the drag torque and temperature field. In order to improve the transmission efficiency and the service life of friction disc, Taguchi experiment and non-dominated neighborhood immune algorithm were used to optimize the structural parameters of the oil grooves. The comparison results show that the optimized structural can effectively reduce the drag torque and the temperature rise. This work can provide a theoretical reference for the structure design of a wet friction clutch. Full article
(This article belongs to the Special Issue Dynamic Stability Analysis of Aerospace Structures)
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17 pages, 4888 KiB  
Article
Efficient Surrogate-Assisted Parameter Analysis for Coal-Supercritical Water Fluidized Bed Reactor with Adaptive Sampling
by Pu Zhao, Haitao Liu, Xinyu Xie, Shiqi Wang, Jiali Liu, Xiaofang Wang, Rong Xie and Siyuan Zuo
Machines 2023, 11(2), 295; https://doi.org/10.3390/machines11020295 - 16 Feb 2023
Cited by 3 | Viewed by 1290
Abstract
Supercritical water fluidized beds (SCWFBs) are promising and efficient reactors for the gasification of coal in supercritical water. The understanding and investigation of multi-phase flows as well as the gasification process usually rely on time-consuming experiments or numerical simulations, which prohibit fast and [...] Read more.
Supercritical water fluidized beds (SCWFBs) are promising and efficient reactors for the gasification of coal in supercritical water. The understanding and investigation of multi-phase flows as well as the gasification process usually rely on time-consuming experiments or numerical simulations, which prohibit fast and full exploration of the single and coupled effects of the operation and geometric parameters. To this end, this paper builds an efficient surrogate-assisted parameter analysis framework for the SCWFB reactor. Particularly, (1) it establishes a steady numerical simulation model of the SCWFB reactor for the subsequent analysis; and (2) it employs a Gaussian process surrogate modeling via efficient adaptive sampling to serve as an approximation for predicting the carbon conversion efficiency (CE) of the reactor. Based on this parameter analysis framework, this paper investigates the effects of five independent parameters (the mass flow rate of supercritical water, mass flow rate of the coal slurry, temperature of supercritical water, temperature of the outer wall and reactor length) and their interactions on the reaction performance in terms of the carbon conversion efficiency (CE). We found that the CE increases as a function of the temperature of supercritical water, the temperature of the outer wall and the reactor length; while it decreases as a function of the mass flow rate of supercritical water and the mass flow rate of the coal slurry. Additionally, the global sensitivity analysis demonstratesthat the influence of the temperature of the outer wall exerts a stronger effect than all the other factors on the CE, and the coupled interaction among parameters has a slight effect on the CE. This research provides useful guidance for scaled-up designs and optimization of the SCWFB reactor. Full article
(This article belongs to the Special Issue Heat Transfer and Energy Harvesting in Fluid System)
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18 pages, 4070 KiB  
Article
Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion
by Jakob Baumgartner, Tadej Petrič and Gregor Klančar
Machines 2023, 11(2), 293; https://doi.org/10.3390/machines11020293 - 16 Feb 2023
Cited by 2 | Viewed by 2763
Abstract
This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall [...] Read more.
This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator. Full article
(This article belongs to the Special Issue Modeling, Sensor Fusion and Control Techniques in Applied Robotics)
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17 pages, 9004 KiB  
Article
Energy Absorption of Square Tubes Filled by Modularized Honeycombs with Multiple Gradients
by Zhen Li, Zhengyang Kang and Xiaoping Su
Machines 2023, 11(2), 294; https://doi.org/10.3390/machines11020294 - 15 Feb 2023
Viewed by 1199
Abstract
The Uniform Honeycomb-filled Tube (UHT) is one of the composite structures that has shown huge potential in absorbing energy. In this paper, Uniform Honeycomb (UH) filler is replaced by an enhanced Modularized Honeycomb (MH). The biggest advantage of MH is that it can [...] Read more.
The Uniform Honeycomb-filled Tube (UHT) is one of the composite structures that has shown huge potential in absorbing energy. In this paper, Uniform Honeycomb (UH) filler is replaced by an enhanced Modularized Honeycomb (MH). The biggest advantage of MH is that it can significantly enhance energy absorption without adding weight compared with its uniform counterpart. Finite element models are created, and then validated by theoretical models. The energy absorption of the Modularized Honeycomb-filled Tube (MHT) is compared with that of the empty tube and UHT. The results show that the MHT is superior to them in Specific Energy Absorption (SEA). It is also found that the tube can help the MH improve its deformation stability, which is the key of the MHT’s excellent energy absorption capacity. Then, effects of design parameters on the SEA of the MHT are investigated and discussed. The results show that the MH with a large graded coefficient is good for enhancing the SEA of the MHT. However, the SEA also relies on the match between the honeycomb filler and tube walls. The work could inspire designs of modularized filler with various types of cells and benefit the development of advanced energy absorbers with lighter weight and more excellent energy absorption capacity. Full article
(This article belongs to the Section Vehicle Engineering)
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19 pages, 3724 KiB  
Article
A Two-Step Approach to Scheduling a Class of Two-Stage Flow Shops in Automotive Glass Manufacturing
by Yan Qiao, Naiqi Wu, Zhiwu Li, Abdulrahman M. Al-Ahmari, Abdul-Aziz El-Tamimi and Husam Kaid
Machines 2023, 11(2), 292; https://doi.org/10.3390/machines11020292 - 15 Feb 2023
Cited by 1 | Viewed by 1103
Abstract
Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at [...] Read more.
Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at the second stage. To the best knowledge of the authors, there is no report on this topic from other research groups. Our previous study presents a method to assign all batches to each machine at the first stage only without sequencing the assigned batches, resulting in an incomplete schedule. To cope with this problem, if a mathematical programming method is directly applied to minimize the makespan of the production process, binary variables should be introduced to describe the processing sequence of all the products, not only the batches, resulting in huge number of binary variables for the model. Thus, it is necessary and challenging to search for a method to solve the problem efficiently. Due to the mandatory requirement that the second stage should keep working continuously without interruption, solution feasibility is essential. Therefore, the key to solve the addressed problem is how to guarantee the solution feasibility. To do so, we present a method to determine the minimal size of each batch such that the second stage can continuously work without interruption if the sizes of all batches are same. Then, the conditions under which a feasible schedule exists are derived. Based on the conditions, we are able to develop a two-step solution method. At the first step, an integer linear program (ILP) is formulated for handling the batch allocation problem at the first stage. By the ILP, we need then to distinguish the batches only, greatly reducing the number of variables and constraints. Then, the batches assigned to each machine at the first stage are optimally sequenced at the second step by an algorithm with polynomial complexity. In this way, by the proposed method, the computational complexity is greatly reduced in comparison with the problem formulation without the established feasibility conditions. To validate the proposed approach, we carry out extensive experiments on a real case from an automotive glass manufacturer. We run ILP on CPLEX for testing. For large-size problems, we set 3600 s as the longest time for getting a solution and a gap of 1% for the lower bound of solutions. The results show that CPLEX can solve 96.83% cases. Moreover, we can obtain good solutions with the maximum gap of 4.9416% for the unsolved cases. Full article
(This article belongs to the Section Industrial Systems)
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18 pages, 4127 KiB  
Article
Lubrication Mechanism of scCO2-MQL in the Assisted Machining of Titanium Alloys
by Limin Shi, Tong Wang, Erliang Liu and Ruyue Wang
Machines 2023, 11(2), 291; https://doi.org/10.3390/machines11020291 - 15 Feb 2023
Cited by 4 | Viewed by 1151
Abstract
Cutting fluids are often used in the machining of titanium alloys to reduce processing temperature and maximize quality and productivity. The permeability of the cutting fluid in the capillary tube directly influences the effect of lubrication on cooling performance. In this study, supercritical [...] Read more.
Cutting fluids are often used in the machining of titanium alloys to reduce processing temperature and maximize quality and productivity. The permeability of the cutting fluid in the capillary tube directly influences the effect of lubrication on cooling performance. In this study, supercritical carbon dioxide cryogenic micro-lubrication (scCO2-MQL) is used for the auxiliary machining of titanium alloys. A capillary model for scCO2-MQL-assisted cutting is proposed and established while considering the characteristics of three-phase states produced during the decompression release of scCO2. The injection temperature and characteristics of scCO2 are experimentally investigated, and the dynamic process of scCO2-MQL penetration into the capillary is analyzed. The results show that under the applied experimental conditions, the injection temperature of scCO2-MQL ranges from approximately −45 °C to 60 °C. Because scCO2 presents good solubility in oil, it has the capacity to refine the oil droplets into smaller particles, thus resulting in a higher lubricating oil content in the capillary per unit of time. This leads to enhanced lubricity that can benefit processing applications. Full article
(This article belongs to the Special Issue Recent Progress of Thin Wall Machining)
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17 pages, 346 KiB  
Article
State Estimation of Positive Switched Interval Systems with Metzler–Takagi–Sugeno Fuzzy Models
by Dušan Krokavec and Anna Filasová
Machines 2023, 11(2), 290; https://doi.org/10.3390/machines11020290 - 15 Feb 2023
Cited by 2 | Viewed by 1124
Abstract
This paper addresses the problem of estimating the state of a class of interval and positive nonlinear switched systems. The considered system class is represented by Metzler–Takagi–Sugeno fuzzy switched models with positive Lipschitz nonlinear functions and bounded disturbance. The fuzzy switching interval observers [...] Read more.
This paper addresses the problem of estimating the state of a class of interval and positive nonlinear switched systems. The considered system class is represented by Metzler–Takagi–Sugeno fuzzy switched models with positive Lipschitz nonlinear functions and bounded disturbance. The fuzzy switching interval observers need real-time measurable values of premise variables. The introduced design method in this paper allows us to compute the lower and upper bounds of the system state under assumption that unknown disturbances are norm-bounded, computing the observer gain to achieve such robustness. Formulations and proofs of the design condition for switching fuzzy positive interval observers document that the diagonal stabilisation principle is implementable by a common set of LMIs in the construction of strictly positive interval observer gains, guaranteeing Metzler and Hurwitz observer system matrices and positiveness of the lower and upper bounds of the estimated system states. Design conditions for the interval-switching observer structures are formulated via linear matrix inequalities to also ensure H-norm disturbance attenuation and corresponding Lipschitz parameter upper bounds. The proposed algorithm structures are informal and easily creatable as is illustrated by a numerical example. Full article
13 pages, 4370 KiB  
Article
Simulation and Verification of Effect of Arc Duration on Arc Temperature Based on COMSOL
by Xiaoying Yu, Mengjie Song and Ze Wang
Machines 2023, 11(2), 289; https://doi.org/10.3390/machines11020289 - 14 Feb 2023
Cited by 3 | Viewed by 1418
Abstract
The pantograph–catenary arc of urban rail has the characteristic of high temperature, which directly acts on the surface of the pantograph–catenary system, will seriously ablate pantograph and contact line, and affect the flow of the pantograph–catenary system. In this paper, based on MHD, [...] Read more.
The pantograph–catenary arc of urban rail has the characteristic of high temperature, which directly acts on the surface of the pantograph–catenary system, will seriously ablate pantograph and contact line, and affect the flow of the pantograph–catenary system. In this paper, based on MHD, a mathematical model of the arcing of urban rail pantograph–catenary system is established. COMSOL finite element software was used to simulate the arc temperature field of the pantograph–catenary arc. Through the temperature distribution cloud diagram and related images, the relationship between the arc central temperature, the duration, and the gap of the pantograph arc were studied. It is found that the arc temperature increases with the increase in arc duration when the arc gap is fixed. When the arc duration is fixed, the arc center temperature changes inversely with the increase in the arc gap. The feasibility of the simulation model is verified by the pantograph–catenary arc experiment platform. The research conclusion of this paper can provide a certain basis for the research of arc erosion on the surface of the pantograph–catenary system. Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
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24 pages, 10042 KiB  
Article
Application of Transient Analysis Techniques to Fault Diagnosis in Low- and Medium-Power Synchronous Machines
by Angela Navarro-Navarro, Jose E. Ruiz-Sarrio, Vicente Biot-Monterde, Jose A. Antonino-Daviu, Vincent Becker and Sven Urschel
Machines 2023, 11(2), 288; https://doi.org/10.3390/machines11020288 - 14 Feb 2023
Cited by 3 | Viewed by 1627
Abstract
Fault diagnosis techniques applied to synchronous motors such as Permanent Magnet Synchronous Machines (PMSMs) and Synchronous Reluctance Machines (SynRMs) are scarcely addressed in the literature, in strong contrast to the attention paid to asynchronous motors. In addition, the most widespread techniques are those [...] Read more.
Fault diagnosis techniques applied to synchronous motors such as Permanent Magnet Synchronous Machines (PMSMs) and Synchronous Reluctance Machines (SynRMs) are scarcely addressed in the literature, in strong contrast to the attention paid to asynchronous motors. In addition, the most widespread techniques are those based on steady-state condition analysis, and little attention is paid to detection during transient operation. The present paper aims to identify research gaps on the topic and to demonstrate the potential of transient analysis. First, the different diagnostic methodologies in literature are thoroughly analyzed. Then, two laboratory case studies are presented to demonstrate the potential of fault detection under non-stationary conditions for a PMSM and a SynRM. Stator current analysis is performed by building time–frequency maps to analyze the evolution of different fault indicators. The results show clear differences between healthy and faulty conditions during the transient regime. Full article
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18 pages, 10212 KiB  
Article
Development of Cost-Effective and Easily Replicable Robust Weeding Machine—Premiering Precision Agriculture in Pakistan
by Azmat Hussain, Hafiza Sundus Fatima, Syed Mohiuddin Zia, Shehzad Hasan, Muhammad Khurram, Didier Stricker and Muhammad Zeshan Afzal
Machines 2023, 11(2), 287; https://doi.org/10.3390/machines11020287 - 14 Feb 2023
Cited by 4 | Viewed by 2895
Abstract
Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. [...] Read more.
Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. The use of agricultural robots will help in the assurance of higher yields and proactive control of the crops. This study proposes a laser-based weeding vehicle with a unique mechanical body that is adjustable relative to the field structure, called the Robot Operating System (ROS) based robust control system, and is customizable, cost-effective and easily replicable. Hence, an autonomous-mobile-agricultural robot with a 20 watt laser has been developed for the precise removal of weed plants. The assembled robot’s testing was conducted in the agro living lab. The field trials have demonstrated that the robot takes approximately 23.7 h at the linear velocity of 0.07 m/s for the weeding of one acre plot. It includes 5 s of laser to kill one weed plant. Comparatively, the primitive weeding technique is highly labor intensive and takes several days to complete an acre plot area. The data presented herein reflects that implementing this technology could become an excellent approach to removing unwanted plants from agricultural fields. This solution is relatively cost-efficient and provides an alternative to expensive human labor initiatives to deal with the increased labor wages. Full article
(This article belongs to the Section Machine Design and Theory)
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