Physics-guided degradation trajectory modeling for remaining useful life prediction of rolling bearings

Remaining useful life (RUL) prediction has great significance in reducing operating costs and enhancing the maintainability and safety of rolling bearings. Recently, significant progress has been achieved in this field by leveraging deep learning approaches. However, advanced deep learning methods suffer from a black-box nature that makes them lack interpretability. Moreover, the prediction results may sometimes not follow laws of physics. To tackle this drawback, a physics-guided degradation trajectory modeling method is proposed for RUL prediction of rolling bearings, where physical knowledge is embedded in input preparation, model construction, and output specification. Specifically, phase space reconstruction is leveraged to construct the physics-guided inputs, transforming the degradation prediction of rolling bearings into the variation estimation of phase space trajectories. The degradation trajectory prediction strategy is derived from the physics-guided inputs and the decreased value of RUL is predicted through a compact one-dimensional convolutional neural network with the nonnegative bounded function. Simulation studies are performed with a theoretical model of defective rolling bearings to illustrate the effectiveness of the constructed physics-guided inputs. In addition, comparative analyses with advanced RUL prediction approaches under unseen working conditions and across different machines are conducted. The results demonstrate that the proposed method outperforms other approaches in both accuracy and robustness, showing its superior ability in RUL prediction of rolling bearings.