Quantile regression network-based cross-domain prediction model for rolling bearing remaining useful life

Abstract

Transfer learning improves remaining useful life (RUL) prediction accuracy across domains by aligning data distributions for different operating conditions. However, the uncertainty caused by the complex working conditions and stochastic degradation process of rolling bearings is not considered, leading to poor credibility of the prediction results and affecting the development of the predictive maintenance strategy. In response to this problem, the paper proposes a deep subdomain adaptation time-quantile regression network (DSATQRN) model to compress rolling bearing uncertainty intervals of RUL across prediction. The model uses deep subdomain adaptation to align the feature distribution and introduces temporal correlation to construct a temporal quantile regression network to obtain interval prediction results. Finally, the uncertainty in the prediction results is quantified by kernel density estimation. The model was experimented with using the open XJTU-SY Bearing Datasets and IEEE PHM 2012 Challenge Datasets. It verifies the performance of the proposed model from three aspects: point prediction accuracy, interval prediction suitability, and probabilistic prediction overall performance. The experimental results show that the average interval coverage of the proposed model on the two datasets is 91.25% and 90.43%, and the average prediction interval width is 16.65% and 13.69%, respectively. It is demonstrated that the cross-domain prediction results of the DSATQRN possess high prediction accuracy and narrow prediction interval, and the model has good robustness and reliability.