Open access
Datum
2022-01Typ
- Journal Article
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 physicsbased 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 runto-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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000506035Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Reliability Engineering & System SafetyBand
Seiten / Artikelnummer
Verlag
ElsevierThema
prognostics; deep learning; hybrid model; CMAPSSOrganisationseinheit
09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
Förderung
176878 - Data-Driven Intelligent Predictive Maintenance of Industrial Assets (SNF)
Zugehörige Publikationen und Daten
Is compiled by: https://doi.org/10.3929/ethz-b-000517153