Auto-Embedding Transformer for Interpretable Few-Shot Fault Diagnosis of Rolling Bearings

Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled data, resulting in poor performance in scenarios with insufficient training samples. Moreover, conventional intel...

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Bibliographic Details
Published in:IEEE transactions on reliability Vol. 73; no. 2; pp. 1270 - 1279
Main Authors: Wang, Gang, Liu, Dongdong, Cui, Lingli
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoencoder
Convolution
Data mining
Deep learning
Diagnostic systems
Embedding
Fault diagnosis
Feature extraction
few-shot diagnosis
interpretability
Modules
Roller bearings
Rolling bearings
Signal quality
transformer
Transformers
Unsupervised learning
Vibrations
ISSN:0018-9529, 1558-1721
Online Access:Get full text
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Summary:Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled data, resulting in poor performance in scenarios with insufficient training samples. Moreover, conventional intelligent diagnosis methods suffer from a deficiency in interpretability. In this article, an auto-embedding transformer (AET) method is proposed to implement the interpretable few-shot fault diagnosis of rolling bearings. First, an auto-embedding module is developed to improve the embedding quality of the signal, which is designed based on a novel asymmetric convolutional encoder-decoder architecture. This module can leverage the merits of unsupervised learning in data mining and allow the transformer to learn more diagnostic knowledge from limited data. Second, an attention scoring method is proposed that utilizes positionwise attention to quantify the importance of each signal embedding for diagnosis, thereby interpreting the AET method. Experimental results confirm that, even with limited training samples, the AET method outperforms various comparison methods in terms of recognition accuracy and convergence rate. Furthermore, the attention scores assigned to each embedding facilitate the interpretability of the AET method.
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ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2023.3328597