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| Unsupervised Fault Diagnosis of Rotating Machinery Based on AVMD and DPC-FCM Algorithm |
| Received:September 20, 2023 Revised:November 02, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2024.01.015 |
| KeyWord:variational mode decomposition fuzzy C-means clustering by fast search and find of density peaks rotating machinery fault diagnosis |
| Author | Institution |
| WU Yaman |
China Shipbuilding Group 705 Research Institute, Xi'an , China |
| CHEN Peng |
China Shipbuilding Group 705 Research Institute, Xi'an , China |
| ZHANG Dian |
China Shipbuilding Group 705 Research Institute, Xi'an , China |
| LIU Tian |
China Shipbuilding Group 705 Research Institute, Xi'an , China |
| TANG Jian |
China Shipbuilding Group 705 Research Institute, Xi'an , China |
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| Abstract: |
| In order to solve the problems of difficult feature extraction of rotating machinery fault signals, less label data in the fault diagnosis process, and low fault diagnosis accuracy, the work aims to propose an unsupervised diagnosis method combining adaptive variational mode decomposition (AVMD) and clustering by fast search and find of density peaks optimizing fuzzy C-means (DPC-FCM).Firstly, the comprehensive coefficient combining multi-scale permutation entropy and kurtosis was proposed as the fitness function to optimize the penalty factor alpha and modal number K of the VMD algorithm. The average sample entropy and average fuzzy entropy of the decomposed intrinsic mode function were extracted and input into the clustering algorithm. Secondly, to solve the problem that FCM clustering algorithm was easy to fall into the local optimal solution, DPC-FCM was proposed to determine the initial clustering centers of FCM to reduce the randomness of clustering results. Finally, the unsupervised fault diagnosis model was constructed and applied to the rolling bearing testing signals, achieving the accurate fault diagnosis. AVMD has advantages in fault extraction, and DPC algorithm can effectively improve the accuracy of unsupervised clustering of FCM algorithm. The combination of the two can effectively realize intelligent classification of rotating machinery faults. |
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