FDPN: a dual-path bearing life prediction method based on frequency domain perception attention

Abstract Accurately predicting the remaining useful life of bearings is of great importance for enabling intelligent operation and health management of industrial equipment. Existing methods often face challenges in jointly modeling degradation trends and transient anomalies. Furthermore, their integration of physical priors remains underexplored, limiting the reliability of predictions. To address these challenges, this paper proposes a novel bearing life prediction framework, termed the frequency-domain perception attention based dual-path network with physics-informed loss (FDPN). The model employs a dual-encoder architecture to separately extract low-frequency degradation-trend features and high-frequency transient-anomaly features, which are adaptively fused via a gated fusion module. Furthermore, a wavelet-based frequency-domain perceptual attention mechanism is designed to explicitly model multi-scale frequency characteristics in vibration signals. Moreover, a physics-informed loss function is constructed by incorporating trend-consistency constraints and an asymmetric error-penalty mechanism, ensuring that the predictions conform to degradation patterns while maintaining safety and reliability. Experimental results on the PHM2012 bearing dataset demonstrate that the proposed FDPN model outperforms mainstream methods, achieving an 8.43% reduction in root mean square error and a 10.70% improvement in safety score (SCORE) compared to the best baseline. These improvements validate FDPN’s superior performance in prediction accuracy, reliability, and engineering safety.