Estimation of rolling bearing remaining useful life using DReLU-RA-ConvGRU model based on the encoder–decoder structure

Abstract

Rolling bearings serve as indispensable core components in modern industrial equipment and they are critical for safety and reliability. Consequently, accurate prediction of their remaining useful life (RUL) is essential and has far-reaching implications. This paper proposes a novel RUL prediction model, referred to as the dynamic rectified linear unit-based residual additive attention ConvGRU (DReLU-RA-ConvGRU) model, which is built upon the encoder–decoder structure to accurately predict the RUL of bearings. To overcome the limitation of the original signal, characterized by a single feature and limited degradation information, three-domain features are employed and filtered as inputs to the model. The DReLU component in the proposed RUL prediction model effectively captures variable feature information within the degraded signal, while the ConvGRU component learns both temporal and spatial information with fewer parameters. Additionally, the RA component captures the significant contributors to RUL prediction, and the inclusion of residuals facilitates easier network learning. Furthermore, a three-dimensional visualization of the attention weights was conducted to enhance the interpretability of the network’s prediction process. In order to verify the effectiveness of the method, RUL prediction was conducted using vibration data from the PRONOSTIA platform and compared against several existing methods. The results demonstrate the method’s superior performance and feasibility, as indicated by high scores.