Few-shot Fault Diagnosis Based on Heterogeneous Information Fusion and Meta-learning

Intelligent fault diagnosis algorithms require large amounts of data to train models, and the fusion of heterogeneous information from multiple sensors increases the computational complexity exponentially. To address these problems, a few-shot cross-domain motor fault diagnosis method based on multi...

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Bibliographic Details
Published in:IEEE sensors journal 2023-09, Vol.23 (18), p.1-1
Main Authors: Zhang, Xiaofei, Tang, Jingbo, Qu, Yinpeng, Qin, Guojun, Guo, Lei, Xie, Jinping, Long, Zhuo
Format: Article
Language:English
Subjects:
Algorithms
Color imagery
Data integration
Demagnetization fault diagnosis
Fault diagnosis
few-shot learning
Machine learning
meta-learning
multi-sensor information fusion
Multisensor fusion
Phase current
Sensors
Training
Working conditions
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Summary:Intelligent fault diagnosis algorithms require large amounts of data to train models, and the fusion of heterogeneous information from multiple sensors increases the computational complexity exponentially. To address these problems, a few-shot cross-domain motor fault diagnosis method based on multi-sensor information fusion and meta-learning is proposed. Firstly, a multi-sensor heterogeneous information fusion framework, named low-pass pyramidal ratio-color symmetric dot pattern (RP-CSDP), is proposed. It enables to achieve the information fusion of three-axis vibration sensor and three-phase current sensor without increasing the computational burden of the intelligent diagnosis algorithm. Secondly, RP-CSDP fuses and reconstructs the data from both types of sensors into color images. Based on this, a meta-learning database is constructed. The Relation Network (RN) is improved, and various cross-working conditions and few-shot experiments are set up. Finally, the proposed method is promoted to diagnose motor faults that are not present in the training phase. The results show that the proposed method can be quickly adapted to new tasks without repeating training network when faced with new working conditions and faulty types with limited training samples.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3299707