{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:08:14Z","timestamp":1784048894324,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB1600604"],"award-info":[{"award-number":["2020YFB1600604"]}]},{"name":"National Key Research and Development Program of China","award":["2020zdzx06-01-01"],"award-info":[{"award-number":["2020zdzx06-01-01"]}]},{"name":"National Key Research and Development Program of China","award":["300102223206"],"award-info":[{"award-number":["300102223206"]}]},{"name":"Major Science and Technology Project of Shaanxi Province","award":["2020YFB1600604"],"award-info":[{"award-number":["2020YFB1600604"]}]},{"name":"Major Science and Technology Project of Shaanxi Province","award":["2020zdzx06-01-01"],"award-info":[{"award-number":["2020zdzx06-01-01"]}]},{"name":"Major Science and Technology Project of Shaanxi Province","award":["300102223206"],"award-info":[{"award-number":["300102223206"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2020YFB1600604"],"award-info":[{"award-number":["2020YFB1600604"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2020zdzx06-01-01"],"award-info":[{"award-number":["2020zdzx06-01-01"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["300102223206"],"award-info":[{"award-number":["300102223206"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013brake 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.<\/jats:p>","DOI":"10.3390\/s23208592","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T07:25:22Z","timestamp":1697786722000},"page":"8592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion"],"prefix":"10.3390","volume":"23","author":[{"given":"Jianping","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dean","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, Q., Wang, M., Lai, W., and Zhao, S. 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