A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions

Abstract

Effectively predicting the remaining useful life (RUL) of rolling bearings can ensure reliability and safety, minimize machine downtime, and reduce the operation and maintenance costs of enterprises. To solve the problems of data distribution discrepancy caused by different working conditions and the collected signals containing a lot of useless information and noise, a novel cross-domain adaption network (CDAN) is proposed in this study. Firstly, a novel feature extractor, squeeze-and-excitation (Se)-selective kernel (Sk)-DenseNet, is developed to extract useful critical features from the input data and remove the ineffective features by embedding Se and Sk attention blocks; besides, a new objective loss function consist of the RUL loss, the multi-kernel maximum mean discrepancy loss, the contrastive loss, and the Kullback–Leibler divergence loss, is proposed to solve the problem of data distribution shift; finally, the effectiveness and superiority of CDAN are proved on the PHM2012 bearings dataset. The results demonstrate that CDAN can extract deep critical features and achieve the high cross-domain RUL prediction accuracy under different working conditions.