A physics-data hybrid driven method for predicting the remaining useful life of rolling bearings

Abstract Addressing the limited adaptability of mechanical models and the deficiencies of data-driven methods in terms of noise robustness and interpretability, this paper proposes a physics and data hybrid driven online remaining useful life (RUL) prediction framework. By establishing a synergistic mapping among defect quantification, vibration features, and performance degradation, it achieves precise RUL prediction for rolling bearings. Specifically, this framework achieves robust quantification of defect size by establishing a bearing failure dynamics model and adopting a dual-objective feature matching method. By combining the quantified defect size with the high-trend time-frequency domain features as the input of the network, a deep synergy between physical degradation laws and vibration responses is achieved. On this basis, a multi-scale deep synergistic network is constructed to extract local transient features and global degradation trends of bearings. Furthermore, a physics-informed loss function with monotonicity constraints is designed to guide model optimization, ensuring that the prediction results conform to the physical mechanism of irreversible degradation of bearings. Experiments conducted on the XJTU-SY and self-built dataset demonstrate that the proposed method significantly outperforms mainstream models in terms of prediction accuracy. The results demonstrate that the deep integration of physical mechanisms and data features effectively enhances the characterization capability of degradation features, providing a novel perspective for the intelligent maintenance of industrial bearings.