Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit

Abstract

Remaining useful life (RUL) prediction plays a crucial role in bearing health management which can guarantee the rotating machinery systems’ safety and reliability. This paper promotes the capability of bearing health management by proposing a novel data-driven prognostic approach. The proposed methodology reveals the underlying deterioration progression by constructing a new health indicator (HI) that fuses distribution-similarity-based multi-scale features. More specifically, a graph named WDgram, to represent the distribution similarity between the analyzed signal and healthy reference at different frequency scales, is developed first using the 1/3-binary tree structure and Wasserstein distance (WD). Then the high-dimensional multi-scale features are fused by the multiobjective grasshopper optimization algorithm (MOGOA) to optimize the HI's monotonicity, robustness, and tenability. By taking advantage of the WDgram and MOGOA, the novel HI is able to thoroughly reveal the underlying degradation characteristics and alleviate random fluctuations which frequently decrease the accuracy of RUL estimation. Subsequently, a gated recurrent unit network, incorporated with the grasshopper optimization algorithm (GOA) to adaptively determine some hyperparameters, is established with high predictive accuracy and generalizability. Experimental validation is conducted to prove the effectiveness of the proposed method. High accuracy and generalizability can be obtained by the proposed method, demonstrating its substantial advantages in health management for rotating machinery systems.