The Transfer Prediction Method of Bearing Remain Use Life Based on Dynamic Benchmark

Abstract

For data-driven remaining useful life (RUL) prediction of rolling bearing, deep learning methods usually do not consider the difference in distribution due to different operating conditions, which adversely affects the prediction results. Recently, transfer learning has been a research hotspot, and it can mitigate the above problem effectively. However, for different bearings under the same working condition, the data distribution changes due to the location and time of failure. To solve these problems, a transfer prediction model based on the dynamic benchmark(DB) has been proposed to predict the RUL of bearings in this study. The method is divided into three processes. First, a fully convolutional variational autoencoder is used to perform unsupervised learning; next, a domain adaptation method based on the DB is employed for reconstruction; finally, extracted hidden layer features are input to the fully connected layer of the proposed model to obtain the final prediction result. The Prediction and Health Management(PHM) Challenging 2012 and XJTU-SY rolling bearing accelerated life test datasets were used for verification. The results showed that the proposed method could significantly improve prediction accuracy and had good stability and generalizability.