基于对比学习的滚动轴承早期故障在线检测方法

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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

Received: 27 April 2022 Published: 28 July 2023

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	Articles by authors
	WANG Yanhong1
	WEN Xiaohuan1
	JIE Yongqin1
	WANG Shaowei2

Cite this article:

WANG Yanhong1,WEN Xiaohuan1,JIE Yongqin1, et al. 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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I14/229

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