A novel method for journal bearing degradation evaluation and remaining useful life prediction under different working conditions

Abstract

Accurate bearing degradation performance analysis and remaining useful life (RUL) prediction are significant to prevent major accidents and economic losses in industry. Data-driven methods have emerged as reliable algorithms for RUL prediction. These existing approaches assume that the training (source) and testing (target) samples have the same probability distribution. However, the obtained run-to-failure datasets with different work conditions are usually from different domains. The distribution discrepancy between the source and target domain will reduce the accuracy of RUL prediction models when only source domain data in one working condition is trained. To solve the problem, this paper proposes a novel transfer learning method for journal bearing RUL prediction under different work conditions based on the LSTM-DNN network with domain adaptation. The multi-sensor run-to-failure datasets of journal bearings are collected and the extracted multi-sensor features are used for degradation assessment through the fuzzy c-means (FCM) clustering algorithm and the determination of degradation occurrence time (DOT). The multi-sensor feature representations and RUL values after DOT are used to validate the effectiveness of the proposed model. The results show that the proposed method has higher accuracy in journal bearing RUL prediction under different work conditions and outperforms other transfer learning approaches.