An Efficient Bearing Prognostic Approach through Modeling Multiperiodic and Nonperiodic Temporal Patterns

Remaining useful life (RUL) prediction of bearings is essential for effective prognostics and health management (PHM). Although deep learning-based RUL prediction methods achieve high prediction accuracy, they often introduce significant parameter redundancy due to their inability to efficiently capture the intricate temporal dynamics in bearing degradation signals, leading to computationally expensive models with limited practical applicability. To address this challenge, we propose a novel RUL prediction framework that integrates the Wasserstein distance of cyclic spectrum (WDCS) with a Lightweight TimesNet (WDCS-LTN). Specifically, the WDCS serves as a health indicator, effectively extracting multiperiodic features from bearing degradation signals. Subsequently, the LTN transforms the 1-D WDCS sequence into multiple 2-D tensors with varying localities, enabling precise modeling of intraperiod and interperiod temporal dynamics. A shared lightweight inception block is constructed within the LTN to capture temporal variations in 2-D space while maintaining low model complexity. Experimental results on bearing degradation datasets show that WDCS-LTN achieves a prediction error (mean absolute error) of 0.091 with only 37k parameters, outperforming existing methods in terms of accuracy, parameter efficiency, and memory consumption. Through efficiently modeling the temporal dynamics, WDCS-LTN ensures practicality for industrial applications by addressing parameter redundancy while offering enhanced prediction capabilities.