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Machines
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Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and...
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Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2491

Special Issue Editors

Prof. Dr. Jinglong Chen

E-Mail Website
Guest Editor
State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: mechanical fault diagnosis; deep learning; signal processing
Dr. Tongyang Pan

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China
Interests: mechanical fault diagnosis; deep learning; signal processing

Special Issue Information

Dear Colleagues,

Electric motors and drives are important power systems for modern industrial equipment.  Facilitated by novel design concepts and advancements in new technologies such as sensing, manufacturing, communication, management, and systems integrity, electric motors and drives have become more sophisticated than ever, making performance prediction and fault diagnosis a challenging problem to ensure reliable operations. However, accurate and timely performance prediction and fault diagnosis are difficult due to the following factors: (i) performance prediction and fault diagnosis is a coupled subject involving modeling analysis, sensing and monitoring, signal processing, and decision making; (ii) it relies on a comprehensive understanding and analysis of the interactive working mechanism under varying environment.

This Special Issue welcomes the submission of new perspectives, theories and algorithms to the challenging problems of performance prediction and fault diagnosis towards electric motors and drives. Research areas may include, but are not limited to, the following topics:

  • Data cleaning and data quality improvement;
  • Advanced modeling techniques;
  • Signal processing and feature extraction;
  • Condition monitoring and health assessment;
  • Data-driven intelligent fault diagnosis and performance prediction;
  • Edge computation for fault diagnosis;
  • Digital-twin-based diagnosis and prediction.

Prof. Dr. Jinglong Chen
Dr. Tongyang Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault diagnosis
  • condition monitoring
  • signal processing
  • deep learning
  • electric motors and drives

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Published Papers (3 papers)

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Research

27 pages, 9212 KiB  
Article
Prior Knowledge-Informed Graph Neural Network with Multi-Source Data-Weighted Fusion for Intelligent Bogie Fault Diagnosis
by Yuanxing Huang, Bofeng Cui, Xianqun Mao and Jinsong Yang
Machines 2024, 12(12), 838; https://doi.org/10.3390/machines12120838 - 22 Nov 2024
Viewed by 267
Abstract
The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded [...] Read more.
The current multi-source fusion fault diagnosis algorithm rarely considers the information correlation of multi-sensor networks and the important difference between multi-sensors. Aiming at this challenge, we propose an intelligent fault identification method for high-speed railway bogie based on the graph neural network embedded with prior knowledge, which brings the spatial information of the sensor network into the diagnosis algorithm and re-weights each sensor according to the diagnosis results. Firstly, the time–domain correlation of vibration signals between bogie sensor networks is calculated as the prior knowledge. Then, based on the spatial topological relationship of the sensors, the graph correlation matrix of the network is established. Further, the importance of each sensor is dynamically analyzed and updated together with the training process. The proposed method is tested on a high-precision bogie test bed, and the experimental results demonstrate the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)
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18 pages, 10016 KiB  
Article
Prediction and Dynamic Simulation Verification of Output Characteristics of Radial Piston Motors Based on Neural Networks
by Chunjin Li, Zhengwen Xia and Yongjie Tang
Machines 2024, 12(7), 491; https://doi.org/10.3390/machines12070491 - 20 Jul 2024
Viewed by 655
Abstract
Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable [...] Read more.
Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable operating conditions. Efficiency determination experiments were conducted on physical models, yielding thirty sets of performance data. Torque (output torque) and mechanical efficiency from the experimental data were selected as prediction targets and fitted using two methods: multiple linear regression and neural networks. A dynamic simulation model was built using Adams2020 software to obtain theoretical torque values, enabling the verification of the alignment between the predicted values and simulation results. The results indicate that the error between the theoretical torque of the dynamic model and the physical experiments is 1.9%, with the error of the neural network predictions being within 2%. The dynamic simulation model can yield highly accurate theoretical torque values, providing a reference for the external load of hydraulic motors; additionally, neural networks offer accurate predictions of output torque, thus reducing experimental testing costs. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)
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17 pages, 8573 KiB  
Article
A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors
by Mohamed Y. Metwly, Victor M. Logan, Charles L. Clark, Jiangbiao He and Biyun Xie
Machines 2024, 12(7), 430; https://doi.org/10.3390/machines12070430 - 24 Jun 2024
Viewed by 971
Abstract
As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield [...] Read more.
As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy. Full article
(This article belongs to the Special Issue Towards Electric Motors and Drives: Condition Monitoring, Performance Prediction and Fault Diagnosis)
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