Combining Approaches of Brownian Motion and Similarity Principle to Improve the Remaining Useful Life Prediction
| Autor: | Dima El Jamal, Jacques Pinaton, Mohammed Al-kharaz, Guillaume Graton, Mustapha Ouladsine, Bouchra Ananou |
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| Přispěvatelé: | Aix Marseille Université (AMU), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Pronostic-Diagnostic Et CommAnde : Santé et Energie (PECASE), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), STMicroelectronics [Rousset] (ST-ROUSSET) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 2021 IEEE International Conference on Prognostics and Health Management (ICPHM) 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), Jun 2021, Detroit (Romulus) (virtual), France. pp.1-7, ⟨10.1109/ICPHM51084.2021.9486507⟩ ICPHM IEEE International Conference on Prognostics and Health Management (ICPHM) |
| Popis: | International audience; This paper proposes a data-driven framework for Remaining Useful Life (RUL) prediction, based on the Brownian Motion model (BM) and the similarity principle, for an operating system given its health indicator. It addresses the issues of noisy and limited run-to-failure (R2F) data. The Percentile filtering is used to extract, from the R2F data, 100 monotonic profiles used as references in the modeling and the RUL prediction. Then, the similarity is computed between these references and the Health Indicator (HI) of the operating system. Fitting the most similar reference into the BM improves the RUL prediction. A numerical application using simulated data justifies the accuracy of this approach. |
| Databáze: | OpenAIRE |
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