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Magenetic Tile Fault Detecion of High Voltage Electitric Machine: A consistent soft-label-based Multi-view feature selection Method
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  • Zuanyuan Yang,
  • Junhang Chen,
  • Mingyang Liu,
  • Daoyuan Li
Zuanyuan Yang
Guangdong University of Technology School of Automation
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Junhang Chen
Guangdong University of Technology School of Automation

Corresponding Author:junhanggdgy@aliyun.com

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Mingyang Liu
Guangdong University of Technology School of Automation
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Daoyuan Li
Guangdong University of Technology School of Automation
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Abstract

The detection of high voltage permanent magnet motors has always been a big problem due to the interference of high voltage and magnetic field on the diagnosis. Especially the magenetic tile of the motor, the failure of the magenetic tile will directly lead to the operation failure of the motor. We propose Multi-view Unsupervised Consistent Soft-label Feature Selection(MUCSFS). This method constructed consistent pseudo-labels through soft labels of clustering affinity of each view sample and constructed the model by integrating selection constraints into the mapping model. This model is used to filter the fault data set to get the feature subset, and the feature subset is used to cluster. We verify the effectiveness of the method by simulating multi-view data and through the fault clustering experiment of the magnetic tile fault data set of high voltage motor, it is confirmed that our method can effectively cluster the fault categories.
17 Jul 2023Submitted to International Journal of Circuit Theory and Applications
19 Jul 2023Submission Checks Completed
19 Jul 2023Assigned to Editor
19 Jul 2023Review(s) Completed, Editorial Evaluation Pending
23 Jul 2023Reviewer(s) Assigned
19 Aug 2023Editorial Decision: Revise Major
09 Oct 20231st Revision Received
17 Oct 2023Submission Checks Completed
17 Oct 2023Assigned to Editor
17 Oct 2023Review(s) Completed, Editorial Evaluation Pending
19 Oct 2023Reviewer(s) Assigned
30 Oct 2023Editorial Decision: Accept