Fusing Physics-based and Deep Learning Models for Prognostics

By Manuel Arias Chao, Chetan S. Kulkarni, Kai F. Goebel, Olga Fink. Reliability Engineering & System Safety, 2020

388 citations recorded by the source corpus.

Data-driven and physics-based models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. The work proposed in the paper describes a novel hybrid framework for fusing the information from physics-based performance models with deep learning algorithms for prognostics of complex safety critical systems under real-world scenarios. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem a.k.a. an inverse problem. These parameters are subsequently combined with sensor readings and used as input to a deep neural network to generate a data-driven prognostics model. The performance of the hybrid framework is evaluated on a synthetic dataset comprising run-to-failure degradation trajectories from a small fleet of nine turbofan engines under a wide range of real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model developed at NASA. The performance of the approach is compared to an alternative data-driven approach where only sensor data are used as input to the deep neural network. A scenario of incomplete representation of the test degradation conditions in the training dataset is evaluated. The experimental results show that the hybrid framework outperforms the alternative pure data-driven approach by extending the prediction horizon by nearly 100% and decreasing the prediction error by 34% based on the applied metrics.

Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/28ec028c2bd8a535fa665e20a1317a6c8835af52

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Citations in the corpus

The challenge and opportunity of battery lifetime prediction from field data (2021, methodology): “This suggests that degradation models could be enhanced with data-driven methods in order to accurately predict the wide range of possible aging mechanisms and cell-to-cell variability [104].”
Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “In the field of fault diagnosis and prognosis, the direction of hybrid modelling and physics-informed approaches has only recently been explored with different directions [205, 11, 206].”
Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021, methodology): “Specifically, dataset DS01 has been used for the application of model-based diagnostics [26] and dataset DS02 has been used for data-driven prognostics [27].”