Fusing physics-based and deep learning models for prognostics

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 f...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 217; p. 107961
Main Authors: Arias Chao, Manuel, Kulkarni, Chetan, Goebel, Kai, Fink, Olga
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.01.2022
Elsevier BV
Subjects:
Algorithms
Artificial neural networks
CMAPSS
Datasets
Deep learning
Drift och underhållsteknik
Flight conditions
Hybrid model
Learning algorithms
Machine learning
Mathematical models
Neural networks
Operation and Maintenance
Parameters
Physics
Prognostics
Reliability engineering
Safety critical
Training
Turbofan engines
Deep learning
CMAPSS
Hybrid model
Prognostics
ISSN:0951-8320, 1879-0836, 1879-0836
Online Access:Get full text
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Summary: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.
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ISSN:0951-8320
1879-0836
1879-0836
DOI:10.1016/j.ress.2021.107961