Online detection method for bearing incipient faults based on contrastive learning

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (14) : 229-236.

WANG Yanhong1,WEN Xiaohuan1,JIE Yongqin1,WANG Shaowei2

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Abstract

Incipient fault detection is not only an important role in prognostics and health management, but also has an increasing practical value in industry. This paper proposes a novel online detection method for bearing incipient fault based on contrastive learning from the perspective of industrial big data. Firstly, a novel deep encoder is proposed based on depth-wise separable convolution and residual connections, which is able to extract features from signal effectively. Then, contrastive learning method is utilized to train the proposed encoder by setting up a specific proxy task. The trained encoder is able to learn differences between signal samples. Finally, a novel online detection algorithm is proposed. The algorithm is able to detect abnormal condition and incipient fault effectively. Experiments are carried out on the XJTU-SY dataset. The results demonstrate our method outperforms existing ones and contributes to enriching bearing health management method under different working conditions.

Key words

Prognostics and health management / Predictive maintenance / Anomaly detection / Incipient fault detection / Contrastive learning / Depth-wise separable convolution

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WANG Yanhong1,WEN Xiaohuan1,JIE Yongqin1,WANG Shaowei2. Online detection method for bearing incipient faults based on contrastive learning[J]. Journal of Vibration and Shock, 2023, 42(14): 229-236

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