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Fusing physics-based and deep learning models for prognostics
Chair of Intelligent Maintenance Systems, ETH Zurich, Switzerland.
KBR, Inc., NASA Ames Research Center, USA.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-0240-0943
Chair of Intelligent Maintenance Systems, ETH Zurich, Switzerland.
2022 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 217, article id 107961Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 217, article id 107961
Keywords [en]
Prognostics, Deep learning, Hybrid model, CMAPSS
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-87121DOI: 10.1016/j.ress.2021.107961ISI: 000702360100002Scopus ID: 2-s2.0-85115029961OAI: oai:DiVA.org:ltu-87121DiVA, id: diva2:1595485
Note

Validerad;2021;Nivå 2;2021-09-20 (alebob);

Forskningsfinansiär: Swiss National Science Foundation (PP00P2 176878)

Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2021-12-13Bibliographically approved

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Goebel, Kai

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