A hybrid data- and model-driven learning framework for remaining useful life prognostics

The efficient and safe production of machinery equipment relies on the health of its mechanical components, making prognostics and health management (PHM) a critical aspect of production processes. One key PHM measure is the remaining useful life (RUL), which estimates the expected lifespan of a component in a production line before requiring repair or replacement. However, state-of-the-art RUL prediction methods, including data-driven, model-based, and hybrid approaches, face limitations such as incomplete/imprecise physical models, uncertainties in degradation processes, and measurement data noise. To address these limitations, this paper proposes a novel hybrid RUL prediction framework that combines the strengths of data-based and model-driven approaches. The framework includes an exponential model to leverage physical knowledge and a multi-head attention transformer to extract information from data. An extended Kalman filter is used to estimate unknown degradation process parameters and provide physical model information for the prediction process. A regression token is introduced to efficiently fuse the deep learning model and the stochastic filtering method. Numerical tests using real-world rolling bearing degradation datasets demonstrate the superiority of the proposed method over competitive alternatives.