{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:47:04Z","timestamp":1773794824916,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":188,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"13th five-year plan of educational informatization construction in Sichuan Province","award":["Chuan Jiao Guan 2019-142"],"award-info":[{"award-number":["Chuan Jiao Guan 2019-142"]}]},{"name":"13th five-year plan of educational informatization construction in Sichuan Province","award":["19YYJSYJ0091"],"award-info":[{"award-number":["19YYJSYJ0091"]}]},{"name":"Aba Science and Technology Bureau","award":["Chuan Jiao Guan 2019-142"],"award-info":[{"award-number":["Chuan Jiao Guan 2019-142"]}]},{"name":"Aba Science and Technology Bureau","award":["19YYJSYJ0091"],"award-info":[{"award-number":["19YYJSYJ0091"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>In order to improve the maintenance efficiency of the motor and realize the real\u2010time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high\u2010frequency signal is denoised to improve the signal\u2010to\u2010noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.<\/jats:p>","DOI":"10.1155\/2021\/3782446","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T23:50:06Z","timestamp":1625788206000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4034-2115","authenticated-orcid":false,"given":"Yong","family":"Yan","sequence":"first","affiliation":[]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiao qin","family":"Gao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2018.2847800"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8843759"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym11101212"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/2050-7038.12737"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"KompellaK. 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