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Processes
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Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance
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Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 3955

Special Issue Editors

Dr. Da Xu

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: electrical engineering & energy and power
Dr. Qiang Li

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: novel permanent magnet machine; motor control technology; wireless power transmission
Dr. Yong Kong

E-Mail Website
Guest Editor
School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, China
Interests: electric machine design; optimization for electric vehicle

Special Issue Information

Dear Colleagues,

In recent years, motor drive systems have become increasingly important in various industrial and commercial applications. These systems are used in a broad range of applications, including electric vehicles, robots, aircraft, and renewable energy systems. As the complexity of these systems increases, the need for effective control technology, fault diagnosis, and fault-tolerance strategies has also grown. Effective control technology is critical for motor drive systems. 

Another critical aspect of motor drive systems is fault diagnosis and fault tolerance. With the increased complexity of motor drive systems, the likelihood of faults occurring has also increased.

This Special Issue, entitled “Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance,” seeks high-quality studies focusing on the most recent advances in control technology for high-fault-tolerance drive systems, including the design, modeling, and optimization of control algorithms for high performance and energy efficiency. Topics include, but are not limited to, the following:

  • Advanced control strategies for motor drive systems, such as sensorless control, model predictive control, and adaptive control;
  • Fault diagnosis techniques for motor drive systems, including signal processing, machine learning, and artificial intelligence approaches;
  • Fault-tolerance strategies for motor drive systems, such as redundancy, fault-tolerant control, and reconfiguration;
  • Integrated design and optimization of motor drive systems, including motors, power electronics, and control systems.

We hope that the articles in this Special Issue will inspire novel research ideas and contribute to further advancements in this exciting field.

Dr. Da Xu
Dr. Qiang Li
Dr. Yong Kong
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. Processes 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

  • motor drive
  • control strategies
  • fault diagnosis
  • fault tolerance
  • integrated design

Published Papers (5 papers)

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Research

21 pages, 3236 KiB  
Article
Fault Diagnosis of Power Transformer in One-Key Sequential Control System of Intelligent Substation Based on a Transformer Neural Network Model
by Cheng Wang, Zhixin Fu, Zheng Zhang, Weiping Wang, Huatai Chen and Da Xu
Processes 2024, 12(4), 824; https://doi.org/10.3390/pr12040824 - 19 Apr 2024
Viewed by 432
Abstract
With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart [...] Read more.
With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy, precision, recall, and F1 score of the PT-TNNet model for equipment state monitoring and fault diagnosis. Finally, using the efficient PT-TNNet, Random Forest, and Extra Trees models, the cross-validation of the accuracy of winding temperature and insulation oil temperature of transformers is performed, confirming the superiority of the PT-TNNet model based on transformer neural networks for power transformer state monitoring and fault diagnosis, its feasibility for application in one-key sequential control systems, and the optimization of one-key sequential control system performance. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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15 pages, 2891 KiB  
Article
Integrating Improved Coati Optimization Algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems
by Kaishi Ji, Azadeh Dogani, Nan Jin and Xuesong Zhang
Processes 2024, 12(3), 479; https://doi.org/10.3390/pr12030479 - 27 Feb 2024
Viewed by 564
Abstract
In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but [...] Read more.
In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced Coati Optimization Algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional Coati Optimization Algorithm (COA). We augment its search capabilities through a dual population strategy, adaptive factors, and a stochastic differential variation strategy. The ICOA is employed for the optimal selection of Bi-LSTM parameters, effectively accomplishing the fault prediction task. Our method harnesses the global search capabilities of the COA and the sophisticated data analysis capabilities of the Bi-LSTM to enhance the accuracy and efficiency of fault warnings. In a practical application to a real-world case of induced draft fan fault warning, our results indicate that our method anticipates faults approximately two hours in advance. Furthermore, in comparison with other advanced methods, namely, the Improved Social Engineering Optimizer Optimized Backpropagation Network (ISEO-BP), the Sparrow Particle Swarm Hybrid Algorithm Optimized Light Gradient Boosting Machine (SSAPSO-LightGBM), and the Improved Butterfly Optimization Algorithm Optimized Bi-LSTM (MSBOA-Bi-LSTM), our proposed approach exhibits distinct advantages and robust prediction effects. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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22 pages, 4795 KiB  
Article
Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm
by Shuai Li, Nan Jin, Azadeh Dogani, Yang Yang, Ming Zhang and Xiangyun Gu
Processes 2024, 12(1), 221; https://doi.org/10.3390/pr12010221 - 19 Jan 2024
Cited by 2 | Viewed by 854
Abstract
The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, [...] Read more.
The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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24 pages, 2718 KiB  
Article
Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm
by Yongsheng Fan, Changshu Zhan and Mohammed Aljuaid
Processes 2023, 11(11), 3057; https://doi.org/10.3390/pr11113057 - 24 Oct 2023
Viewed by 700
Abstract
Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has [...] Read more.
Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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17 pages, 6847 KiB  
Article
Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion
by Fengyun Xie, Gang Li, Qiuyang Fan, Qian Xiao and Shengtong Zhou
Processes 2023, 11(10), 2862; https://doi.org/10.3390/pr11102862 - 28 Sep 2023
Viewed by 956
Abstract
Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The [...] Read more.
Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability. Full article
(This article belongs to the Special Issue Motor Drive Systems: Control Technology, Fault Diagnosis and Fault Tolerance)
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