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Remaining useful life interval prediction of bearing based on CNN model

Published:2023-10-19 author:ZHOU Ming-zhu, ZHANG Yi-bao, WU Shuang, et al. Browse: 1059 Check PDF documents

Remaining useful life interval prediction of bearing based on CNN model

ZHOU Ming-zhu1, ZHANG Yi-bao1, WU Shuang2, KONG Li-jun1, WANG Zi-qi3,4

(1.Inner Mongolia Huomei Hongjun Aluminum Power Co., Ltd., Tongliao 029200, China; 2.Hunan Zhongrong Huizhi Information

Technology Co., Ltd., Changsha 410221, China; 3.School of Control Science and Engineering, Zhejiang University, Hangzhou

310027, China; 4.Huzhou Institute of Zhejiang University, Huzhou 313002, China)

Abstract: Aiming at the problem of uncertainty quantification of remaining useful life (RUL) prediction of bearings, integrating both data uncertainty and model uncertainty, an interval prediction method on RUL of bearings based on convolutional neural network (CNN) was proposed. Firstly, the input data was preprocessed to extract the time domain features of the vibration signal, and the parameters with the strong trend were selected as the input of the model. Then,a CNN model with a normal distribution placed in the output layer was designed for the point prediction and data uncertainty capture. After that, an ensemble method was used to quantify the model uncertainty and output the interval prediction results. Finally, the effectiveness of the proposed method was validated using the published PHM2012 bearing degradation dataset, and the results were prepared with those obtained by Bayesian neural network (BNN). The experiment result indicates that,in the application of RUL prediction of bearings, the proposed method has the highest prediction interval coverage probability (PICP),63.9% higher than that of BNN, while it holds the smallest root mean squared error (RMSE)of the point prediction with 0.1997. The results indicate that, the proposed interval prediction method on RUL of bearings based on CNN has greater advantages in describing the uncertainty of prediction while ensuring the accuracy of point prediction estimation, which has more significance in practice.

Key words: rolling bearing; remaining useful life (RUL); interval prediction; uncertainty quantification; convolutional neural network (CNN); prediction interval coverage probability (PICP)

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