Fusing physics-based and deep learning models for prognostics - Arias Chao, Manuel; Kulkarni, Chetan; Goebel, Kai; Fink, Olga (Reliability engineering & system safety, 2022)
Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited representativeness of the training dataset for data-driven models. Combining the advantages of these two approaches while overcoming some of their limitations, we propose a novel hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety-critical systems. In the proposed framework, we use physics-based performance models to infer unobservable model parameters related to a system’s components health by solving a calibration problem. These parameters are subsequently combined with sensor readings and used as input to a deep neural network, thereby generating a data-driven prognostics model with physics-augmented features. The performance of the hybrid framework is evaluated on an extensive case study comprising run-to-failure degradation trajectories from a fleet of nine turbofan engines under real flight conditions. The experimental results show that the hybrid framework outperforms purely data-driven approaches by extending the prediction horizon by nearly 127%. Furthermore, it requires less training data and is less sensitive to the limited representativeness of the dataset as compared to purely data-driven approaches. Furthermore, we demonstrated the feasibility of the proposed framework on the original CMAPSS dataset, thereby confirming its superior performance. •Novel hybrid framework for prognostics of complex safety-critical systems proposed.•The framework combines deep learning and physics-based performance models.•Deep neural networks are trained with physics-augmented features for RUL prediction.•Framework evaluated on the new CMPASS aero-engine degradation dataset.•The proposed framework outperforms equivalent purely data-driven approaches.