Machines

19 pages, 9549 KiB

Open AccessArticle

Reinforcement Learning Control of Hydraulic Servo System Based on TD3 Algorithm

by Xiaoming Yuan, Yu Wang, Ruicong Zhang, Qiang Gao, Zhuangding Zhou, Rulin Zhou and Fengyuan Yin

Machines 2022, 10(12), 1244; https://doi.org/10.3390/machines10121244 - 19 Dec 2022

Cited by 8 | Viewed by 2668

Abstract

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can [...] Read more.

This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a hydraulic servo system. An intelligent control method is designed that uses self-learning without a model or prior knowledge, in order to achieve certain control effects. The control quantity can be obtained at the current moment through the continuous iteration of a strategy–value network, and the online self-tuning of parameters can be realized. Taking the hydraulic servo system as the experimental object, a twin delayed deep deterministic (TD3) policy gradient was used to reinforce the learning of the system. Additionally, the parameter setting was compared using a deep deterministic policy gradient (DDPG) and a linear–quadratic–Gaussian (LQG) based on linear quadratic Gaussian objective function. To compile the reinforcement learning algorithm and deploy it to the test platform controller for testing, we used the Speedgoat prototype target machine as the controller to build the fast prototype control test platform. MATLAB/Coder and compute unified device architecture (CUDA) were used to generate an S-function. The results show that, compared with other parameter tuning methods, the proposed algorithm can effectively optimize the controller parameters and improve the dynamic response of the system when tracking signals. Full article

(This article belongs to the Topic Designs and Drive Control of Electromechanical Machines)

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14 pages, 2602 KiB

Open AccessArticle

Orthogonal Experimental Design Based Nozzle Geometry Optimization for the Underwater Abrasive Water Jet

by Xiangyu Wang, Yongtao Wu, Peng Jia, Huadong Liu, Feihong Yun, Zhibo Li and Liquan Wang

Machines 2022, 10(12), 1243; https://doi.org/10.3390/machines10121243 - 19 Dec 2022

Cited by 5 | Viewed by 1511

Abstract

This paper proposes an orthogonal experimental design based on the optimization method for the nozzle geometry of an underwater abrasive water jet, with the objective of maximizing the cutting capacity and minimizing the nozzle-erosion rate. Parameter effects on the nozzle’s cutting capability and [...] Read more.

This paper proposes an orthogonal experimental design based on the optimization method for the nozzle geometry of an underwater abrasive water jet, with the objective of maximizing the cutting capacity and minimizing the nozzle-erosion rate. Parameter effects on the nozzle’s cutting capability and life are analyzed. This analysis shows that while the contraction-section curve, the contraction-section axial length and the focus-section axial length mainly affected the service life of the nozzle, the nozzle-outlet diameter mainly affected the cutting capacity of the nozzle. The effect significances of the structural parameters, from high to low, are outlet diameter > axial length of contraction section > axial length of focusing section > contraction curve. According to the optimal performance index for this nozzle, the optimal nozzle structure parameters were a contraction-section curve of

A_{4}

(parabolic), an axial length of contraction section of 20 mm, an outlet diameter of 2 mm, and an axial length focusing section of 10 mm. With the optimal parameters, the nozzle performance excellence index was

Q

= 1.441, which is the optimization objective and 44.1% higher than the baseline of the conical nozzle; the maximum velocity at a distance of 100 mm was improved by 56% and the maximum erosion rate was reduced by 72% compared to that of the conical nozzle. Full article

(This article belongs to the Section Advanced Manufacturing)

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18 pages, 1527 KiB

Open AccessArticle

Modelling of Determinants of Logistics 4.0 Adoption: Insights from Developing Countries

by Shahbaz Khan, Rubee Singh, José Carlos Sá, Gilberto Santos and Luís Pinto Ferreira

Machines 2022, 10(12), 1242; https://doi.org/10.3390/machines10121242 - 19 Dec 2022

Cited by 3 | Viewed by 2234

Abstract

With the emergence of industry 4.0, several elements of the supply chain are transforming through the adoption of smart technologies such as blockchain, the internet of things and cyber-physical systems. Logistics is considered one of the important elements of supply chain management and [...] Read more.

With the emergence of industry 4.0, several elements of the supply chain are transforming through the adoption of smart technologies such as blockchain, the internet of things and cyber-physical systems. Logistics is considered one of the important elements of supply chain management and its digital transformation is crucial to the success of industry 4.0. In this circumstance, the existing logistics system needs to be upgraded with industry 4.0 technologies and emerge as logistics 4.0. However, the adoption/transformation of logistics 4.0 is dependent on several determinants that need to be explored. Therefore, this study has the prime objective of investigating the determinants of logistics 4.0 adoption in the context of a developing country, specifically, India. Initially, ten determinants of logistics 4.0 are established after a survey of the relevant literature and the input of industry experts. Further, a four-level structural model is developed among these determinants using the Interpretive Structural Modelling (ISM) approach. In addition, a fuzzy Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis is also conducted for the categorization of these determinants as per their driving and dependence power. The findings show that top management supports, information technology infrastructure and financial investment are the most significant determinants towards logistics 4.0 adoption. This study facilitates the supply chain partners to focus on these high-level determinants for the effective adoption of logistics 4.0. Moreover, the findings lead to a more in-depth insight into the determinants that influence logistics 4.0 and their significance in logistics 4.0 adoption in emerging economies. Full article

(This article belongs to the Special Issue Lean Manufacturing and Industry 4.0)

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16 pages, 2289 KiB

Open AccessArticle

Design and Analysis of Mechanical Characteristics of EAP Flexible Drivers

by Bing Li, Shaohua Niu, Bingyang Li, Pengfei Wang and Yuli Qiao

Machines 2022, 10(12), 1241; https://doi.org/10.3390/machines10121241 - 19 Dec 2022

Viewed by 1224

Abstract

Electroactive polymer(EAP) is a “smart material” with high energy density, high electromechanical energy conversion efficiency, simple structure, good adaptability to the working environment, etc. It can be made into various shapes to realize flexible drivers. At present, the common EAP actuator is mainly [...] Read more.

Electroactive polymer(EAP) is a “smart material” with high energy density, high electromechanical energy conversion efficiency, simple structure, good adaptability to the working environment, etc. It can be made into various shapes to realize flexible drivers. At present, the common EAP actuator is mainly composed of EAP film wound on a spring, and the output performance of this type of actuator is related to the spring stiffness, film prestretching rate, voltage, and other factors. Its working process is actually an electromechanical coupling process. In this paper, two types of cylindrical actuators are designed and tested. The electromechanical coupling mathematical model is constructed to simulate the driver. According to the experimental and simulation results, the relationship between the output displacement and elongation strain of EAP actuator and voltage, spring stiffness, and tensile rate is analyzed. It provides a reference and basis for the design of similar actuators. Full article

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18 pages, 2352 KiB

Open AccessArticle

Combination of Thermal and Mechanical Strategies to Compensate for Distortion Effects during Profile Grinding

by Christian Schieber, Matthias Hettig, Michael Friedrich Zaeh and Carsten Heinzel

Machines 2022, 10(12), 1240; https://doi.org/10.3390/machines10121240 - 19 Dec 2022

Viewed by 1171

Abstract

This paper describes the investigations and the results of an analysis of distortion compensation processes for profile grinding. Steel workpieces often change their residual stress state due to machining in a seemingly uncontrolled matter. Furthermore, in research as well as in the industry, [...] Read more.

This paper describes the investigations and the results of an analysis of distortion compensation processes for profile grinding. Steel workpieces often change their residual stress state due to machining in a seemingly uncontrolled matter. Furthermore, in research as well as in the industry, the accurate representation of shape deviations during the cutting of slim profiled workpieces and their deformation handling is a major challenge. In this paper, a valid predictive model, developed for the compensation of distortions resulting from the effect of a laser-based treatment and a deep rolling, was calibrated by experimental data. The numerical design of these strategies provided a model for predicting compensation parameters to minimize profile grinding distortions. Full article

(This article belongs to the Section Advanced Manufacturing)

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17 pages, 9091 KiB

Open AccessArticle

Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures

by Jing Wei, Zhengtao Zhang, Fei Shen and Chengkan Lv

Machines 2022, 10(12), 1239; https://doi.org/10.3390/machines10121239 - 18 Dec 2022

Cited by 1 | Viewed by 2418

Abstract

Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in [...] Read more.

Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in defect segmentation tasks. To tackle these problems, we proposed a mask-guided background-preserving defect generation method, MDGAN (mask-guided defect generation adversarial networks). First, to preserve the normal background and provide accurate annotations for the generated defect samples, we proposed a background replacement module (BRM), to add real background information to the generator and guide the generator to only focus on the generation of defect content in specified regions. Second, to guarantee the quality of the generated complex texture defects, we proposed a double discrimination module (DDM), to assist the discriminator in measuring the realism of the input image and distinguishing whether or not the defects were distributed at specified locations. The experimental results on metal, fabric, and plastic products showed that MDGAN could generate diversified and high-quality defect samples, demonstrating an improvement in detection over the traditional augmented samples. In addition, MDGAN can transfer defects between datasets with similar defect contents, thus achieving zero-shot defect detection. Full article

(This article belongs to the Special Issue Social Manufacturing on Industrial Internet)

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20 pages, 9353 KiB

Open AccessArticle

Nonlinear Analysis of Rotor-Bearing-Seal System with Varying Parameters Muszynska Model Based on CFD and RBF

by Rui Wang, Yuefang Wang, Xiaojian Cao, Shuhua Yang and Xinglin Guo

Machines 2022, 10(12), 1238; https://doi.org/10.3390/machines10121238 - 18 Dec 2022

Viewed by 1117

Abstract

The computational fluid dynamics (CFD) combined with radial basis function (RBF) method were adopted to obtain the response surface of the Muszynska nonlinear seal force model coefficient with two variables: eccentricity and rotation speed. During the implementation of the simulation, three coefficients of [...] Read more.

The computational fluid dynamics (CFD) combined with radial basis function (RBF) method were adopted to obtain the response surface of the Muszynska nonlinear seal force model coefficient with two variables: eccentricity and rotation speed. During the implementation of the simulation, three coefficients of the seal force model were calculated in each sub-step according to the current state of the rotor-bearing seal system; following which the rotor dynamics analysis with varying parameters was realized. As with the traditional constant coefficient method, the first-order critical speed of the system was obtained, and the bifurcation point and oil film whirl of the system response were identified. The difference is that the coefficients of the traditional method ordinarily do not change with the state of the system. Comparing the results of the varying parameter method with those of the traditional method, it can be seen that the speeds of the system corresponding to the bifurcation and oil film whirl are different. The varying parameter rotor dynamics simulation method proposed in this paper provides a new way of analyzing the nonlinear characteristics of rotor-bearing-seal systems. Full article

(This article belongs to the Special Issue Selected Papers from CITC2022)

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26 pages, 17991 KiB

Open AccessArticle

Thermal Properties Prediction of Large-Scale Machine Tool in Vacuum Environment Based on the Parameter Identification of Fluid–Thermal Coupling Model

by Tianjian Li, Guobin Xi, Han Wang, Wa Tang, Zhongxi Shao and Xizhi Sun

Machines 2022, 10(12), 1237; https://doi.org/10.3390/machines10121237 - 18 Dec 2022

Cited by 1 | Viewed by 1342

Abstract

A high vacuum environment safeguards the performance of special processing technologies and high-precision parts such as nanosecond laser processing, chip packaging, and optical components. However, it poses higher requirements for the machine tool, which makes the temperature control of machine tools an important [...] Read more.

A high vacuum environment safeguards the performance of special processing technologies and high-precision parts such as nanosecond laser processing, chip packaging, and optical components. However, it poses higher requirements for the machine tool, which makes the temperature control of machine tools an important goal in design and development. In this paper, the thermal properties of a large-scale 5-axis laser processing machine tool in a vacuum environment were investigated. The thermal contact resistance between parts is identified by the parametric simulation and experiment. The whole machine temperature field was then obtained based on the fluid–thermal coupling model and verified by experiment. The results showed that the thermal contact resistance of the motor and reducer with the water cold plate was 560 W/(m²∙°C) and 510 W/(m²∙°C), respectively, and the maximum temperature increase of the machine was 3 °C. Based on the results, the machine tool’s temperature increase prediction chart was obtained by simulation under different processing conditions such as cooling water flow rate, cooling water temperature, motor speed, and ambient temperature. It provides technical and data references for the research on the thermal stability of the machine tool in processing. Full article

(This article belongs to the Section Advanced Manufacturing)

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23 pages, 5675 KiB

Open AccessArticle

A Transferable Thruster Fault Diagnosis Approach for Autonomous Underwater Vehicle under Different Working Conditions with Insufficient Labeled Training Data

by Baoji Yin, Ziwei Wang, Mingjun Zhang, Zhikun Jin and Xing Liu

Machines 2022, 10(12), 1236; https://doi.org/10.3390/machines10121236 - 17 Dec 2022

Cited by 3 | Viewed by 1174

Abstract

Existing thruster fault diagnosis methods for AUV (autonomous underwater vehicle) usually need sufficient labeled training data. However, it is unrealistic to get sufficient labeled training data for each working condition in practice. Based on this challenge, a transferable thruster fault diagnosis approach is [...] Read more.

Existing thruster fault diagnosis methods for AUV (autonomous underwater vehicle) usually need sufficient labeled training data. However, it is unrealistic to get sufficient labeled training data for each working condition in practice. Based on this challenge, a transferable thruster fault diagnosis approach is proposed. In the approach, an IPSE (instantaneous power spectrum entropy) and a STNED (signal-to-noise energy difference) are added to SPWVD (smoothed pseudo Wigner-Ville distribution) to identify time and frequency boundaries of the local region in the time-frequency power spectrum caused by thruster fault, forming a TFE (time-frequency energy) method for feature extraction. In addition, the RCQFFV (relative change quantity of the fault feature value), an MSN (multiple scale normalization) and a LSP (least square prediction) are added to SVDD (support vector data description) to align distributions of fault samples, contributing a TSVDD (transferable SVDD) for classification of fault samples. The experimental results of a prototype AUV indicate that the fault feature is monotonic to the percentage of thrust loss for the proposed TFE but not for the SPWVD. The TSVDD has a higher overall classification accuracy in comparison to conventional SVDD under working conditions with no labeled training data. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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20 pages, 7216 KiB

Open AccessArticle

Coordinated Control Strategy of Electro-Hydraulic Composite Braking Torque for the Distributed Electric Vehicles

by Zhigang Zhou, Xiaofei Yin and Jie Zhang

Machines 2022, 10(12), 1235; https://doi.org/10.3390/machines10121235 - 16 Dec 2022

Viewed by 1505

Abstract

The difference in response to electric and hydraulic braking causes sudden changes in braking torque during braking mode switching. An electro-hydraulic composite braking system’s dynamic torque coordination control strategy is proposed under braking mode switching conditions. By establishing the dynamic response model of [...] Read more.

The difference in response to electric and hydraulic braking causes sudden changes in braking torque during braking mode switching. An electro-hydraulic composite braking system’s dynamic torque coordination control strategy is proposed under braking mode switching conditions. By establishing the dynamic response model of the electro-hydraulic braking system (EHB), the key factors affecting the response speed of the EHB are analyzed, and the dynamic fuzzy controller for the pressure regulation of the brake wheel cylinder is designed. At the same time, the nonlinearity and hysteresis in the hydraulic braking process are considered, as well as electrical brake response overshoots. The electric brake response model is established, and the PID controller with feedforward feedback is designed to control the motor to adjust the inertia overpressure or lag pressure deficiency in the hydraulic braking process. Finally, the simulation verification is carried out; the results show that the proposed strategy can increase the hydraulic brake response speed by 25.4%, the impact degree of the vehicle is not more than 6.25 GB, and the hydraulic steady state error does not exceed 2.3%, which improves the vehicle ride comfort under braking mode switching. Full article

(This article belongs to the Topic Vehicle Dynamics and Control)

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17 pages, 3476 KiB

Open AccessEditor’s ChoiceArticle

A General Pose Recognition Method and Its Accuracy Analysis for 6-Axis External Fixation Mechanism Using Image Markers

by Sida Liu, Yimin Song, Binbin Lian and Tao Sun

Machines 2022, 10(12), 1234; https://doi.org/10.3390/machines10121234 - 16 Dec 2022

Viewed by 1464

Abstract

The 6-axis external fixation mechanism with Gough-Stewart configuration has been widely applied to the correction of long bone deformities in orthopedics. Pose recognition of the mechanism is essential for trajectory planning of bone correction, but is usually implemented by the surgeons’ experience, resulting [...] Read more.

The 6-axis external fixation mechanism with Gough-Stewart configuration has been widely applied to the correction of long bone deformities in orthopedics. Pose recognition of the mechanism is essential for trajectory planning of bone correction, but is usually implemented by the surgeons’ experience, resulting in a relatively low level of correction accuracy. This paper proposes a pose recognition method based on novel image markers, and implements accuracy analysis. Firstly, a pose description of the mechanism is established with several freely installed markers, and the layout of the markers is also parametrically described. Then, a pose recognition method is presented by identifying the orientation and position parameters using the markers. The recognition method is general in that it encompasses all possible marker layouts, and the recognition accuracy is investigated by analyzing variations in the marker layout. On this basis, layout principles for markers that achieve a desired recognition accuracy are established, and an error compensation strategy for precision improvement is provided. Finally, experiments were conducted. The results show that volume errors of pose recognition were 0.368 ± 0.130 mm and 0.151 ± 0.045°, and the correction accuracy of the fracture model after taking compensation was 0.214 ± 0.573 mm and −0.031 ± 0.161°, validating the feasibility and accuracy of the proposed methods. Full article

(This article belongs to the Special Issue Development and Applications of Parallel Robots)

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27 pages, 2120 KiB

Open AccessArticle

Machine Learning in CNC Machining: Best Practices

by Tim von Hahn and Chris K. Mechefske

Machines 2022, 10(12), 1233; https://doi.org/10.3390/machines10121233 - 16 Dec 2022

Cited by 4 | Viewed by 5779

Abstract

Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work [...] Read more.

Building machine learning (ML) tools, or systems, for use in manufacturing environments is a challenge that extends far beyond the understanding of the ML algorithm. Yet, these challenges, outside of the algorithm, are less discussed in literature. Therefore, the purpose of this work is to practically illustrate several best practices, and challenges, discovered while building an ML system to detect tool wear in metal CNC machining. Namely, one should focus on the data infrastructure first; begin modeling with simple models; be cognizant of data leakage; use open-source software; and leverage advances in computational power. The ML system developed in this work is built upon classical ML algorithms and is applied to a real-world manufacturing CNC dataset. The best-performing random forest model on the CNC dataset achieves a true positive rate (sensitivity) of 90.3% and a true negative rate (specificity) of 98.3%. The results are suitable for deployment in a production environment and demonstrate the practicality of the classical ML algorithms and techniques used. The system is also tested on the publicly available UC Berkeley milling dataset. All the code is available online so others can reproduce and learn from the results. Full article

(This article belongs to the Special Issue Safety of Machinery: Design, Monitoring, Manufacturing)

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31 pages, 101067 KiB

Open AccessArticle

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

by Lijun Qiao, Xiao Luo and Qingsheng Luo

Machines 2022, 10(12), 1232; https://doi.org/10.3390/machines10121232 - 16 Dec 2022

Cited by 1 | Viewed by 1972

Abstract

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on [...] Read more.

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

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25 pages, 15528 KiB

Open AccessArticle

Experimental and Numerical Study of Collision Attitude Auxiliary Protection Strategy for Subway Vehicles

by Ping Xu, Liting Yang, Weinian Guo, Chengxing Yang, Quanwei Che and Tuo Xu

Machines 2022, 10(12), 1231; https://doi.org/10.3390/machines10121231 - 16 Dec 2022

Viewed by 1340

Abstract

An auxiliary protection device (rail holding mechanism) was proposed to control the collision attitude of subway vehicles. The dynamics model of head-on collision of subway vehicles was established and verified by the full-scale collision test of the real car; then the force element [...] Read more.

An auxiliary protection device (rail holding mechanism) was proposed to control the collision attitude of subway vehicles. The dynamics model of head-on collision of subway vehicles was established and verified by the full-scale collision test of the real car; then the force element structure of the rail holding mechanism was equated; finally, the vertical lift and the pitch angle of the three characteristic sections of car body and the wheelsets were used as the evaluation indicators to study the effects of the three design parameters: the gap distance (

x_{1}

), the linear stage distance (

Δ x_{2}

) and the stiffness of linear stage (

k_{1}

). The results show that the linear stage distance has little influence on the collision attitude of the car body, while the

x_{1}

and

k_{1}

had a greater influence on the collision attitude of the car body. The reasonable reduction of the gap distance

x_{1}

and increase the

k_{1}

can effectively reduce the vertical lift of the wheelsets and alleviate the nodding phenomenon of the train, and reduce the derailment and jumping phenomenon during the train collision. Full article

(This article belongs to the Section Vehicle Engineering)

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20 pages, 1174 KiB

Open AccessArticle

Constraint Definition for Gripper Selection and Grasp Planning for Robotic Assembly Using Product Manufacturing Information from STEP AP242Ed2 Files

by Shafi Khurieshi Mohammed, Mathias Hauan Arbo and Lars Tingelstad

Machines 2022, 10(12), 1230; https://doi.org/10.3390/machines10121230 - 16 Dec 2022

Cited by 1 | Viewed by 1867

Abstract

This article uses the Product Manufacturing Information (PMI) from STEP AP242 neutral files for gripper selection and grasp planning in a robotic assembly operation. The PMI, along with the part geometry and dimensions, are used in identifying various handling features of the parts [...] Read more.

This article uses the Product Manufacturing Information (PMI) from STEP AP242 neutral files for gripper selection and grasp planning in a robotic assembly operation. The PMI, along with the part geometry and dimensions, are used in identifying various handling features of the parts and selecting an appropriate gripper. The required PMI, like material, volume, surface finish, threading and coating information, are added to the STEP AP242 files. The PMI is semantically included in the STEP files following the Model Based Definition (MBD) methodology. Two methods are described to add the PMI to the STEP files, one using a custom string and another using the standard entities defined in ISO 10303 AP242: 2020 standard. The entire process is demonstrated in a use case. Full article

(This article belongs to the Topic Robotics and Automation in Smart Manufacturing Systems)

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15 pages, 6325 KiB

Open AccessArticle

Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings

by Jianmin Zhou, Xiaotong Yang, Lulu Liu, Yunqing Wang, Junjie Wang and Guanghao Hou

Machines 2022, 10(12), 1229; https://doi.org/10.3390/machines10121229 - 16 Dec 2022

Cited by 1 | Viewed by 1379

Abstract

Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the [...] Read more.

Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis. Full article

(This article belongs to the Special Issue Advances in Bearing Modeling, Fault Diagnosis, RUL Prediction)

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28 pages, 11288 KiB

Open AccessArticle

State Estimation of Memristor Neural Networks with Model Uncertainties

by Libin Ma and Mao Wang

Machines 2022, 10(12), 1228; https://doi.org/10.3390/machines10121228 - 15 Dec 2022

Cited by 1 | Viewed by 1056

Abstract

This paper is concerned with the problem of state estimation of memristor neural networks with model uncertainties. Considering the model uncertainties are composed of time-varying delays, floating parameters and unknown functions, an improved method based on long short term memory neural networks (LSTMs) [...] Read more.

This paper is concerned with the problem of state estimation of memristor neural networks with model uncertainties. Considering the model uncertainties are composed of time-varying delays, floating parameters and unknown functions, an improved method based on long short term memory neural networks (LSTMs) is used to deal with the model uncertainties. It is proved that the improved LSTMs can approximate any nonlinear model with any error. On this basis, adaptive updating laws of the weights of improved LSTMs are proposed by using Lyapunov method. Furthermore, for the problem of state estimation of memristor neural networks, a new full-order state observer is proposed to achieve the reconstruction of states based on the measurement output of the system. The error of state estimation is proved to be asymptotically stable by using Lyapunov method and linear matrix inequalities. Finally, two numerical examples are given, and simulation results demonstrate the effectiveness of the scheme, especially when the memristor neural networks with model uncertainties. Full article

(This article belongs to the Section Automation and Control Systems)

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20 pages, 6347 KiB

Open AccessArticle

Model-Based Field Winding Interturn Fault Detection Method for Brushless Synchronous Machines

by Kumar Mahtani, José M. Guerrero, Luis F. Beites and Carlos A. Platero

Machines 2022, 10(12), 1227; https://doi.org/10.3390/machines10121227 - 15 Dec 2022

Cited by 2 | Viewed by 1363

Abstract

The lack of available measurements makes the detection of electrical faults in the rotating elements of brushless synchronous machines particularly challenging. This paper presents a novel and fast detection method regarding interturn faults at the field winding of the main machine, which is [...] Read more.

The lack of available measurements makes the detection of electrical faults in the rotating elements of brushless synchronous machines particularly challenging. This paper presents a novel and fast detection method regarding interturn faults at the field winding of the main machine, which is characterized because it is non-intrusive and because its industrial application is straightforward as it does not require any additional equipment. The method is built upon the comparison between the theoretical and the measured exciter field currents. The theoretical exciter field current is computed from the main machine output voltage and current magnitudes for any monitored operating point by means of a theoretical healthy brushless machine model that links the main machine with the exciter. The applicability of the method has been verified for interturn faults at different fault severity levels, both through computer simulations and experimental tests, delivering promising results. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Electrical Machines and Drives)

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20 pages, 8190 KiB

Open AccessArticle

A Novel Combination Neural Network Based on ConvLSTM-Transformer for Bearing Remaining Useful Life Prediction

by Feiyue Deng, Zhe Chen, Yongqiang Liu, Shaopu Yang, Rujiang Hao and Litong Lyu

Machines 2022, 10(12), 1226; https://doi.org/10.3390/machines10121226 - 15 Dec 2022

Cited by 5 | Viewed by 3361

Abstract

A sensible maintenance strategy must take into account the remaining usable life (RUL) estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In view of some inherent drawbacks of traditional CNN and LSTM-based RUL prognostics models, a novel combination model of the [...] Read more.

A sensible maintenance strategy must take into account the remaining usable life (RUL) estimation to maximize equipment utilization and avoid costly unexpected breakdowns. In view of some inherent drawbacks of traditional CNN and LSTM-based RUL prognostics models, a novel combination model of the ConvLSTM and the Transformer, which is based on the idea of “Extracting spatiotemporal features and applying them to RUL prediction”, is proposed for RUL prediction. The ConvLSTM network can directly extract low-dimensional spatiotemporal features from long-time degradation signals. The Transformer, based entirely on attention mechanisms, can deeply explore the mapping law between deep-level nonlinear spatiotemporal feature information and equipment service performance degradation. The proposed approach is validated with the whole-life degradation dataset of bearings from the PHM 2012 Challenge dataset and the XJTU-SY public dataset. The detailed comparative analysis shows that the proposed method has higher RUL prediction accuracy and outstanding comprehensive prediction performance. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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21 pages, 12924 KiB

Open AccessEditor’s ChoiceArticle

Sliding Mode Based Load Frequency Control and Power Smoothing of Power Systems with Wind and BESS Penetration

by Zhiwen Deng, Chang Xu, Zhihong Huo, Xingxing Han and Feifei Xue

Machines 2022, 10(12), 1225; https://doi.org/10.3390/machines10121225 - 15 Dec 2022

Cited by 5 | Viewed by 1589

Abstract

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. [...] Read more.

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. A novel comprehensive control framework is proposed for power systems integrated with wind farms and BESS based on an adaptive fuzzy super-twisting sliding mode control (AF-SSMC) method. Firstly, the sliding functions and control laws of subsystems are designed according to different relative degrees. Then, the super-twisting algorithm is applied to suppress the chattering of the sliding mode control law. Furthermore, an adaptive fuzzy control method is used to adjust the control gains online for better control performance of the controllers. The Lyapunov stability theory is employed to prove the asymptotic stability of the subsystems. The model of an interconnected thermal power system with wind and BESS penetration is also constructed for simulation analyses. The results indicate that the AF-SSMC method effectively reduces the chattering, and the proposed framework stabilizes the frequency of the power system under system uncertainties and external disturbances. Moreover, the wind farm and BESS combined system accurately tracks a reference power to reduce wind power fluctuations. Full article

(This article belongs to the Special Issue Optimization and Control of Distributed Energy Systems)

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24 pages, 7596 KiB

Open AccessArticle

New Anti-Derailment System in Railway Crossings

by Antonio J. Sala, Jesus Felez, Juan de Dios Sanz and Jaime Gonzalez

Machines 2022, 10(12), 1224; https://doi.org/10.3390/machines10121224 - 15 Dec 2022

Cited by 3 | Viewed by 1510

Abstract

The objective of this paper is to design a new system to reduce the risk of derailment at crossings, which are critical points in railway lines. Crossings are a common element in conventional lines of current railway systems and are the only point [...] Read more.

The objective of this paper is to design a new system to reduce the risk of derailment at crossings, which are critical points in railway lines. Crossings are a common element in conventional lines of current railway systems and are the only point on the track where there is a discontinuity. Our proposal is based on adding an element to the crossing that occupies part of the crossing gap, providing a larger support surface next to the wing rail, such that the wheel does not fall into the gap. The lateral force—which is the most influential parameter in derailments—is substantially decreased, thus reducing the risk of derailment due to lifting on the rail. The proposed approach also increases the safety of the dynamic behaviour, which has a direct impact on passenger comfort and influences the service life of both the rolling stock and the track, thus reducing the cost and even increasing safety at higher speeds. It has a simple structure that is easy to assemble and does not interrupt traffic during installation. The results of simulations using this innovative solution indicate a significant reduction in lateral stresses and strains on the track, which undoubtedly produces an improvement in traffic safety; however, the results cannot be fully quantified in terms of accident reduction with only the data obtained from simulations. Therefore, it was concluded that implementation of the new crossing design provides better conditions for rolling stock to run on turnouts, increasing safety by reducing the risk of derailment. Nevertheless, it will be necessary to carry out a program of experimental tests, which we intend to make the subject of future research. Full article

(This article belongs to the Section Vehicle Engineering)

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19 pages, 6091 KiB

Open AccessArticle

Minimizing Misalignment and Frame Protrusion of Shoulder Exoskeleton via Optimization for Reducing Interaction Force and Minimizing Volume

by Jihwan Yoon, Sumin Kim, Junyoung Moon, Jehyeok Kim and Giuk Lee

Machines 2022, 10(12), 1223; https://doi.org/10.3390/machines10121223 - 15 Dec 2022

Cited by 4 | Viewed by 1716

Abstract

Although industrial shoulder exoskeletons have undergone rapid advancement, their acceptance by industrial workers is limited owing to the misalignment and interference between the exoskeletal frame and the wearer’s body and bulkiness of the frames. Several joint mechanisms have been developed to offset misalignments; [...] Read more.

Although industrial shoulder exoskeletons have undergone rapid advancement, their acceptance by industrial workers is limited owing to the misalignment and interference between the exoskeletal frame and the wearer’s body and bulkiness of the frames. Several joint mechanisms have been developed to offset misalignments; however, none of the existing systems can simultaneously alleviate the interference and bulkiness problems. Furthermore, the reduction in the misalignments in terms of forces generated at the human–robot interface has not been experimentally verified. Therefore, in this study, design optimization was performed to address the various factors that limit the use of the existing industrial shoulder exoskeletons. Upper body motions were captured and converted into a target trajectory for the exoskeleton to follow. The optimal prismatic–revolute–revolute joint configuration was derived and used to manufacture a skeletal mock-up, which was used to perform experiments. The misalignments of the optimized configuration in the considered motions were 67% lower than those for the conventional joint configuration. Furthermore, the interaction forces were negligible (1.35 N), with a maximum reduction of 61.8% compared to those of conventional configurations. Full article

(This article belongs to the Special Issue Advances and Challenges in Wearable Robotics for Dynamic and Varied Tasks)

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23 pages, 11218 KiB

Open AccessArticle

A Control Method of Mobile Manipulator Based on Null-Space Task Planning and Hybrid Control

by Shijun Zhang, Shuhong Cheng and Zhenlin Jin

Machines 2022, 10(12), 1222; https://doi.org/10.3390/machines10121222 - 15 Dec 2022

Cited by 1 | Viewed by 1952

Abstract

The mobile manipulator is a floating base structure with wide space operability. An integrated mechanical device for mobile operation is formed through the organic combination of the mobile platform and multi-axis manipulator. This paper presents a general kinematic modeling method for mobile manipulators [...] Read more.

The mobile manipulator is a floating base structure with wide space operability. An integrated mechanical device for mobile operation is formed through the organic combination of the mobile platform and multi-axis manipulator. This paper presents a general kinematic modeling method for mobile manipulators and gives the relevant derivation of the dynamic model. Secondly, the null-space composition of the mobile manipulator is analyzed, the task space is divided, and a variety of task-switching criteria are designed. Finally, a hybrid control model combining dynamic feedback and synovial control based on dynamic parameter identification is designed, and stability proof is given. The theoretical method is also verified by the experimental platform. The proposed method can effectively improve the control accuracy of the mobile manipulator, and the hybrid control method can effectively control the output torque to reach the ideal state. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation)

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17 pages, 2409 KiB

Open AccessEditor’s ChoiceArticle

Indirect Estimation of Tire Pressure on Several Road Pavements via Interacting Multiple Model Approach

by Renato Brancati and Francesco Tufano

Machines 2022, 10(12), 1221; https://doi.org/10.3390/machines10121221 - 15 Dec 2022

Cited by 3 | Viewed by 1533

Abstract

Generally, tire deflation results in a decrease in both handling performance and tire lifetime, and in fuel consumption increment. Therefore, the real-time knowledge of the pressure is important. Direct approaches via pressure sensors mounted on the rim of each tire are not practical, [...] Read more.

Generally, tire deflation results in a decrease in both handling performance and tire lifetime, and in fuel consumption increment. Therefore, the real-time knowledge of the pressure is important. Direct approaches via pressure sensors mounted on the rim of each tire are not practical, due to technical and economic reasons. Cost-effective solutions with real-time estimation of tire pressure are generally less accurate and reliable than direct ones. Dynamical estimators based on a suspension model need road surface topology information to compute disturbances on the suspension system as an input, which is typically unknown. This paper proposes an innovative approach to estimate tire pressure indirectly, without actual road surface roughness information. A vertical suspension dynamic model is used to build several unscented Kalman filters, parametrised around different road surface topologies. These estimators are combined following the Interacting Multiple Model approach, which gives an acceptable estimation of tire stiffness through a weighted average obtained from a probabilistic model. A known linear static relationship between the tire stiffness and inflation pressure is utilized to indirectly estimate the tire inflation pressure. A Monte Carlo analysis has been performed on a wide range of driving scenarios and vehicle manoeuvres. The results of the estimation have been compared to those of a single unscented Kalman filter, in order to validate the effectiveness of the proposed solution and to highlight the improved performances in monitoring tire pressure. Full article

(This article belongs to the Special Issue Feature Review Papers on Trends and Challenges in Automotive Engineering)

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16 pages, 7640 KiB

Open AccessArticle

Gear Wheel Finishing with Abrasive Brushing Tools to Improve the Surface Quality of Tooth Flanks for the Industrial Application

by Bernhard Gülzow and Eckart Uhlmann

Machines 2022, 10(12), 1220; https://doi.org/10.3390/machines10121220 - 15 Dec 2022

Cited by 2 | Viewed by 1529

Abstract

A high surface quality of tooth flanks can improve the service life and the performance of gears, as well as reduce acoustic emissions. However, high demands on the gear geometry pose a challenge for the finishing of tooth flank surfaces because the dimensional [...] Read more.

A high surface quality of tooth flanks can improve the service life and the performance of gears, as well as reduce acoustic emissions. However, high demands on the gear geometry pose a challenge for the finishing of tooth flank surfaces because the dimensional accuracy that can be achieved with modern grinding processes must not be impaired by the finishing process. A preceding study has shown fundamentally that profiled abrasive brushing tools can be used to improve the quality of individual tooth flank surfaces. Due to the integration into the grinding machine, it represents a promising alternative to common finishing applications. Before the process can be used in an industrial environment, process reliability and tool life must be examined. For this purpose, complete reference gearwheels (39 × 10) were finished with the brushing tools. It could be shown that the surface roughness can be reliably reduced by ΔRa ≈ 0.2 µm by using a single brush for an entire gearwheel without changing the gear geometry. In addition to the influence of the tool specifications on the work result, the influence of the initial roughness after grinding was considered in particular. It was found that the achievable surface roughness depends significantly on the depth of the grinding grooves, as these are retained as desired, while the roughness peaks are fully smoothed. Furthermore, a device for the machine-integrated profiling and dressing of brushing tools was successfully designed, implemented, and tested. Full article

(This article belongs to the Special Issue Advances and Trends in Non-conventional, Abrasive and Precision Machining 2022)

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19 pages, 4566 KiB

Open AccessArticle

Elastostatic Stiffness Modeling and Performance Evaluation of a 2UPR–2PRU Redundantly Actuated Parallel Manipulator

by Xinxue Chai, Wei Ye, Qinchuan Li and Lingmin Xu

Machines 2022, 10(12), 1219; https://doi.org/10.3390/machines10121219 - 15 Dec 2022

Cited by 1 | Viewed by 1043

Abstract

Redundantly actuated parallel manipulators (PMs) have attracted a great deal of attention since they generally have better stiffness than non-redundantly actuated ones. This paper presents an analytical elastostatic stiffness modeling and performance study of a 2UPR–2PRU PM with actuation redundancy, which has two [...] Read more.

Redundantly actuated parallel manipulators (PMs) have attracted a great deal of attention since they generally have better stiffness than non-redundantly actuated ones. This paper presents an analytical elastostatic stiffness modeling and performance study of a 2UPR–2PRU PM with actuation redundancy, which has two rotational and one translational degrees of freedom (U: universal joint; P: prismatic joint; R: revolute joint). First, the inverse displacement is reviewed and verified briefly. Second, the stiffness matrices of UPR and PRU limbs are deduced by using the principle of strain energy, followed by the overall stiffness matrix of the 2UPR–2PRU PM. Combined with the ANSYS software, the finite element analysis method is then used to verify the correctness and universality of the stiffness models by calculating the deformations of four selected configurations. Finally, the stiffness index based on the virtual work is used to evaluate the performance of the 2UPR–2PRU PM, and the influence of different external loads and operational heights on the stiffness performance is discussed. The relationship between singular configurations and the stiffness index is also presented. The stiffness models and performance distributions of the 2UPR–2PRU PM with actuation redundancy can provide references for the actual applications. Full article

(This article belongs to the Special Issue Development and Applications of Parallel Robots)

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24 pages, 13039 KiB

Open AccessArticle

Load Distribution and Dynamic Response in Torque Split Applications

by Süleyman Emre Civan and Cihan Demir

Machines 2022, 10(12), 1218; https://doi.org/10.3390/machines10121218 - 15 Dec 2022

Viewed by 2481

Abstract

This study consists of constructing and analyzing gear mathematical models of torque split systems for contact pressure distribution and dynamic transmission error at different gear positions concerning phase angles. According to the method specified in the AGMA 927 standard, load distribution is calculated [...] Read more.

This study consists of constructing and analyzing gear mathematical models of torque split systems for contact pressure distribution and dynamic transmission error at different gear positions concerning phase angles. According to the method specified in the AGMA 927 standard, load distribution is calculated by considering shaft torsion and bending deformations. Partial contact loss may occur as a result of shaft bending with asymmetric gear positioning on a long shaft. The contact separation can be decreased by reaction force balancing if the driven gears are in the opposite position with respect to the drive gear. In the calculation of the dynamic transmission error of the torque split model, a parametric phase difference for the gear positions is proposed using gear geometry parameters. The variation of the dynamic response according to the change in the parametric phase angle in the torque split system is analyzed for the same values of each gear. Small changes in the phase values change the system response significantly. To obtain lower dynamic transmission error amplitude, the phase difference and gear positions are examined. The contact pressure distribution is validated by the finite element method, and the dynamic transmission error is compared with the experimental study in the literature. Full article

(This article belongs to the Special Issue Vibration and Acoustic Analysis of Components and Machines)

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18 pages, 4785 KiB

Open AccessArticle

Optimal Stator and Rotor Slots Design of Induction Motors for Electric Vehicles Using Opposition-Based Jellyfish Search Optimization

by Ahamed Ibrahim Sithy Juhaniya, Ahmad Asrul Ibrahim, Muhammad Ammirrul Atiqi Mohd Zainuri, Mohd Asyraf Zulkifley and Muhammad Akmal Remli

Machines 2022, 10(12), 1217; https://doi.org/10.3390/machines10121217 - 14 Dec 2022

Cited by 3 | Viewed by 2033

Abstract

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer [...] Read more.

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor. Full article

(This article belongs to the Special Issue Advanced and Efficient Electric Propulsion Systems)

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14 pages, 4106 KiB

Open AccessArticle

Design and Performance Investigation of a Vehicle Drive System with a 12/10 Flux-Switching Permanent Magnet Motor

by Yada Chi, Guangyuan Shi, Haorong Guo, Nan Yang, Chengcheng Zhu and Minchao Cui

Machines 2022, 10(12), 1216; https://doi.org/10.3390/machines10121216 - 14 Dec 2022

Viewed by 1404

Abstract

The performance of a drive system with a flux-switching permanent magnet (FSPM) motor was studied through tests on a commercial electric vehicle (CEV). A practical design and an optimization method for the FSPM motor were proposed for a light-duty CEV. The initial dimensions [...] Read more.

The performance of a drive system with a flux-switching permanent magnet (FSPM) motor was studied through tests on a commercial electric vehicle (CEV). A practical design and an optimization method for the FSPM motor were proposed for a light-duty CEV. The initial dimensions of the motor were calculated by theoretical equations referring to a permanent magnet synchronous motor. Then, optimization was conducted through a response surface methodology (RSM) and a genetic algorithm (GA) based on three-dimensional finite element analysis (3D-FEA). With the optimized parameters, a prototype of the FSPM drive system was manufactured and assembled into an actual CEV. The performance of the CEV was investigated on an automobile test platform. The experimental results show that the FSPM drive system could drive the CEV properly. The high-efficiency running time of the FSPM motor accounted for 84% of the total time tested, which shows great potential for practical application in CEVs. However, the experimental results also show that the FSPM motor faced problems of large speed deviation and high-temperature rise during the driving cycle test, which should be fully addressed for practical applications. Full article

(This article belongs to the Special Issue Advanced Electric Machines, Drives and Applications for Electric Vehicles)

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18 pages, 2931 KiB

Open AccessArticle

Dynamics Analysis and Deep Learning-Based Fault Diagnosis of Defective Rolling Element Bearing on the Multi-Joint Robot

by Wentao Zhang, Ting Zhang, Guohua Cui and Ying Pan

Machines 2022, 10(12), 1215; https://doi.org/10.3390/machines10121215 - 14 Dec 2022

Viewed by 1537

Abstract

Industrial robots typically perform a variety of tasks and occupy critical positions in modern manufacturing fields. When certain failures occur in the internal structures of robots, it tends to result in significant financial loss and the consumption of human resources. As a result, [...] Read more.

Industrial robots typically perform a variety of tasks and occupy critical positions in modern manufacturing fields. When certain failures occur in the internal structures of robots, it tends to result in significant financial loss and the consumption of human resources. As a result, timely and effective fault diagnosis is critical to ensuring the safe operation of robots. Deep learning-based methods are currently being widely used by researchers to address some shortcomings of traditional methods. However, due to realistic factor limitations, few researchers take the failure pattern of rotating machinery and the location of fault joints into account at the same time, so the fault types of multi-joint robots are not thoroughly investigated. In this case, we proposed a dynamic simulation method that considers the effects of bearing failures at various faulty joint locations and makes it possible to investigate more possible failure cases of multi-joint robots. In addition, we used LSTM and DCNN to perform staged fault diagnosis tasks, allowing us to gradually locate faulty joints and investigate detailed failure forms. According to the experimental results, distinguishable vibration signals corresponding to various fault states are successfully obtained, and our implemented algorithms are validated for their advanced performance in diagnosis tasks via comparative experiments. Full article

(This article belongs to the Special Issue Feature Extraction and Condition Monitoring in Physics and Mechanics)

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Machines, Volume 10, Issue 12 (December 2022) – 139 articles

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