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Particle filter-based approach to estimate remaining useful life for predictive maintenance

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-319-19066-2_67
AuteurRechercher : 1; Rechercher : ; Rechercher : ; Rechercher : 2; Rechercher :
Affiliation
  1. Conseil national de recherches du Canada. Technologies de l'information et des communications
  2. Conseil national de recherches du Canada
FormatTexte, Chapitre de livre
Conférence28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2015), June 10-12, 2015, Seoul, South Korea
Sujetparticle filter (PF); remaining useful life (RUL); predictive maintenance (PM); predictive model; APU starter prognostics
Résumé
Estimation of remaining useful life (RUL) plays a vital role in performing predictive maintenance for complex systems today. However, it still remains a challenge. To address this issue, we propose a Particle filter (PF)- based method to estimate remaining useful life for predictive maintenance by employing PF technique to update the nonlinear predictive models for forecasting system states. In particular, we applied PF techniques to estimate remaining useful life by integrating data-driven modeling techniques in order to effectively perform predictive maintenance. After introducing the PF-based algorithm, the paper presents the implementation along with the experimental results through a case study of Auxiliary Power Unit (APU) starter prognostics. The results demonstrated that the developed method is useful for estimating RUL for predictive maintenance.
Date de publication
Maison d’éditionSpringer International Publishing
Dans
  • Current Approaches in Applied Artificial Intelligence: 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings : 692–701.
Série
  • Lecture Notes in Computer Science, nº 9101.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC21276982
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Identificateur de l’enregistrement89c8f0e2-94c6-42e0-acec-34f6d7a605aa
Enregistrement créé2015-11-10
Enregistrement modifié2020-06-11
Date de modification :