A method for predicting the remaining useful life of rolling bearings under different working conditions based on multi-domain adversarial networks

Abstract

Predicting the remaining useful life (RUL) of rolling bearings under different working conditions improved significantly by transfer learning. However, existing methods have not studied the following problems thoroughly: (1) The influence of the discrepancy between features of different dimensions on the feature transfer process; (2) The feature transfer process in the degradation stage with apparent discrepancy has a significant influence on the transfer prediction of remaining useful life. In this study, a degradation occurrence time identification method based on the distribution differences in reconstructing degradation indicators has been proposed to obtain samples of degradation stages. A stack convolutional autoencoder model based on a multi-domain adversarial network is also proposed to reduce the impact of discrepancies among extracted degradation features on the feature transfer process. As per the experimental results, it was found that the proposed method can effectively improve the RUL prediction accuracy.