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Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing f...

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Autores principales: Wang, Jianping, Ma, Jian, Meng, Dean, Zhao, Xuan, Zhang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610660/
https://www.ncbi.nlm.nih.gov/pubmed/37896685
http://dx.doi.org/10.3390/s23208592
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author Wang, Jianping
Ma, Jian
Meng, Dean
Zhao, Xuan
Zhang, Kai
author_facet Wang, Jianping
Ma, Jian
Meng, Dean
Zhao, Xuan
Zhang, Kai
author_sort Wang, Jianping
collection PubMed
description Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.
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spelling pubmed-106106602023-10-28 Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion Wang, Jianping Ma, Jian Meng, Dean Zhao, Xuan Zhang, Kai Sensors (Basel) Article Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors. MDPI 2023-10-20 /pmc/articles/PMC10610660/ /pubmed/37896685 http://dx.doi.org/10.3390/s23208592 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jianping
Ma, Jian
Meng, Dean
Zhao, Xuan
Zhang, Kai
Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title_full Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title_fullStr Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title_full_unstemmed Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title_short Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion
title_sort fault diagnosis of pmsms based on image features of multi-sensor fusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610660/
https://www.ncbi.nlm.nih.gov/pubmed/37896685
http://dx.doi.org/10.3390/s23208592
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