Dual-Attention-Based Multiscale Convolutional Neural Network With Stage Division for Remaining Useful Life Prediction of Rolling Bearings

Abstract

Remaining useful life (RUL) prediction of rolling bearings is of great importance in improving the reliability and durability of rotating machinery. This paper proposes a dual-attention-based convolutional neural network with accurate stage division for rolling bearings RUL prediction, which includes two subsections, i.e., First prediction time (FPT) determination and RUL estimation. Firstly, signal features characterizing the bearing degradation process are fused by Wasserstein Distance to perform two stage division with great robustness. The correct labeled RUL samples with explicit degradation property are then prepared for future network training. Dual attention mechanism is adopted to not only focus on the effect of different sensor signals but also different time steps. Afterwards, multiscale convolution is utilized to both extract local and global weighted features to obtain more comprehensive information. Finally, several convolutional blocks are applied to further obtain accurate RUL prediction. The results derived from fault-mechanism-based simulation signals and experimental signals show that the proposed method is more effective and robust by ablation and comparison study.