一种基于TC-CAE的轴承寿命预测方法

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Abstract When predicting the remaining useful life (RUL) of a bearing, the ability to effectively extract the degradation features is one of the keys to achieve accurate prediction. The individual heterogeneity of the bearing and the difference in operating conditions lead to different degradation characteristic curves, and the change trend of the same characteristic is different on different bearings, which leads to the mismatch between the RUL prediction model established by the training bearing and the test bearing. The individual variability of the bearing should be considered when extracting features. Reducing the individual variability of bearing features is beneficial to improve the prediction accuracy. In order to promote the trend consistency of the same feature on different bearings and reduce the individual bearing differences of degraded features, a bearing life prediction based on trend consistency convolutional auto-encoder (TC-CAE) is proposed. method. By constructing trend consistency constraints and combining with convolutional autoencoding, a TC-CAE feature extraction model is formed. The prediction process is to first use the TC-CAE model to extract features in the frequency domain signal, and then use Long Short-Term Memory (LSTM) to predict. Experiments on a bearing public data set, the experimental results show that: Compared with the prediction results of the ordinary convolutional autoencoding method, the comprehensive average error of the method is reduced by 21.1%, compared with the feature evaluation method and the convolutional neural network method. respectively reduced by 35.6% and 25.9%.
Key words: bearing; life prediction; feature extraction; trend consistency

Key words： bearing life prediction feature extraction trend consistency

Received: 25 May 2021 Published: 28 July 2022

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	Articles by authors
	LI Hailang
	LIU Yongzhi
	ZOU Yisheng
	LIU Yantao
	SONG Xiaoxin

Cite this article:

LI Hailang,LIU Yongzhi,ZOU Yisheng, et al. Bearing life prediction based on the method of TC-CAE[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(14): 105-113.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I14/105

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