A deep learning-based two-stage prognostic approach for remaining useful life of rolling bearing

Remaining useful life (RUL) prediction is of great importance to improve the reliability and availability of machinery. Traditional rolling bearing RUL prediction methods ignore the impact of bearing degradation states recognition on accurate the RUL prediction. A novel deep learning-based two-stage RUL prognostic approach is presented in this paper by using fast search and find of density peaks clustering (FSFDPC) and multi-dimensional deep neural network (MDDNN). In the first stage, health states of the rolling bearing are automatically perceived by the FSFDPC, where no manual parameters need to be preset. In the second stage, MDDNN is constructed with parallel BLSTM and BGRU channels to extract the multi-dimensional feature maps from the input for accurate RUL prediction. The effectiveness of the proposed two-stage prognostic approach is validated by rolling bearing experimental data of three cases. The comparisons with other existing approaches show that the proposed approach has superior prediction performance under different working conditions.