Fusing physics-based and deep learning models for prognostics. (January 2022)

• Record Type: Journal Article
• Authors: Arias Chao, Manuel; Kulkarni, Chetan; Goebel, Kai; Fink, Olga
• Is Part Of: Reliability engineering & system safety. Volume 217(2022)
• Publication Date: 2022-01
• Subjects: Prognostics -- Deep learning -- Hybrid model -- CMAPSS; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107961
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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. Highlights: 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.