State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

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TitreState of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels
Type de publicationJournal Article
Year of Publication2017
AuteursJaved K, Gouriveau R, Zerhouni N
JournalMECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume94
Pagination214-236
Date PublishedSEP 15
Type of ArticleReview
ISSN0888-3270
Mots-clésApplicability, Data processing, Modeling, Prediction, Prognostics, reliability, robustness, Uncertainty
Résumé

Integrating prognostics to a real application requires a certain maturity level and for this reason there is a lack of success stories about development of a complete Prognostics and Health Management system. In fact, the maturity of prognostics is closely linked to data and domain specific entities like modeling. Basically, prognostics task aims at predicting the degradation of engineering assets. However, practically it is not possible to precisely predict the impending failure, which requires a thorough understanding to encounter different sources of uncertainty that affect prognostics. Therefore, different aspects crucial to the prognostics framework, i.e., from monitoring data to remaining useful life of equipment need to be addressed. To this aim, the paper contributes to state of the art and taxonomy of prognostics approaches and their application perspectives. In addition, factors for prognostics approach selection are identified, and new case studies from component-system level are discussed. Moreover, open challenges toward maturity of the prognostics under uncertainty are highlighted and scheme for an efficient prognostics approach is presented. Finally, the existing challenges for verification and validation of prognostics at different technology readiness levels are discussed with respect to open challenges. (C) 2017 Published by Elsevier Ltd.

DOI10.1016/j.ymssp.2017.01.050