Motor Fault Diagnosis Using Attention-Based Multisensor Feature Fusion

Zhuoyao Miao, Wenshan Feng, Zhuo Long (), Gongping Wu, Le Deng, Xuan Zhou and Liwei Xie
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Zhuoyao Miao: International College of Engineering, Changsha University of Science & Technology, Changsha 410114, China
Wenshan Feng: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Zhuo Long: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Gongping Wu: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Le Deng: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Xuan Zhou: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
Liwei Xie: School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Energies, 2024, vol. 17, issue 16, 1-17

Abstract: In order to reduce the influence of environmental noise and different operating conditions on the accuracy of motor fault diagnosis, this paper proposes a capsule network method combining multi-channel signals and the efficient channel attention (ECA) mechanism, sampling the data from multiple sensors and visualizing the one-dimensional time-frequency domain as a two-dimensional symmetric dot pattern (SDP) image, then fusing the multi-channel image data and extracting the image using a capsule network combining the ECA attention mechanism features to match eight different fault types for fault classification. In order to guarantee the universality of the suggested model, data from Case Western Reserve University (CWRU) is used for validation. The suggested multi-channel signal fusion ECA attention capsule network (MSF-ECA-CapsNet) model fault identification accuracy may reach 99.21%, according to the experimental findings, which is higher than the traditional method. Meanwhile, the method of multi-sensor data fusion and the use of the ECA attention mechanism make the diagnosis accuracy much higher.

Keywords: capsule network; motor fault diagnosis; multi-channel fusion; symmetric dot pattern (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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