A physics-guided multi-scale attention fusion network for bearing remaining useful life prediction

Abstract Bearings are critical components that ensure the normal operation of rotating machinery predicting its remaining useful life (RUL) is essential for proactive maintenance and safeguarding production reliability. However, current bearing RUL prediction methods often face challenges such as incomplete acquisition of degradation information and physical inconsistency in prediction results. To resolve these problems, this paper presents a physics-guided multi-scale attention fusion network (PMSAFN) for RUL prediction of bearings. Specifically, a multi-scale attention fusion (MSAF) network is designed to simultaneously capture both global degradation trends and local fault evolution patterns from historical monitoring data of the bearing. By incorporating rotary position encoding, the proposed method can encode bearing features, which significantly enhances its ability to process long-sequence features. At the same time, a gated fusion mechanism is designed to dynamically integrate global and local features, ensuring a comprehensive representation of bearing health states. Furthermore, a physics-informed adaptive penalty loss function is constructed to guide the results to comply with the physical laws related to bearing degradation. Comparative experiments and ablation experiments based on the XJTU-SY dataset and PHM 2012 challenge dataset show that the PMSAFN network achieves better prognostic accuracy.