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Discovering patterns for prognostics: a case study in prognostics of train wheels

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-642-21822-4_18
AuteurRechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
FormatTexte, Chapitre de livre
Conférence24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2011), June 28-July 1, 2011, Syracuse, NY, USA
Sujetdata mining; time-series; reliable patterns; utility; prognostics
Résumé
Data-driven prognostic for system health management represents an emerging and challenging application of data mining. The objective is to develop data-driven prognostic models to predict the likelihood of a component failure and estimate the remaining useful lifetime. Many models developed using techniques from data mining and machine learning can detect the precursors of a failure but sometimes fail to precisely predict time to failure. This paper attempts to address this problem by proposing a novel approach to find reliable patterns for prognostics. A reliable pattern can predict state transitions from current situation to upcoming failures and therefore help better estimate the time to failure. Using techniques from data mining and time-series analysis, we developed a KDD methodology for discovering reliable patterns from multi-stream time-series databases. The techniques have been applied to a real-world application: train prognostics. This paper reports the developed methodology along with preliminary results obtained on prognostics of wheel failures on train.
Date de publication
Maison d’éditionSpringer Berlin Heidelberg
Dans
  • Modern Approaches in Applied Intelligence: 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Syracuse, NY, USA, June 28 – July 1, 2011, Proceedings, Part I : 165–175.
Série
  • Lecture Notes in Computer Science, nº 6703.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC20494949
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Identificateur de l’enregistrement95667e55-10fe-4ad4-83ba-80ba3613cdc2
Enregistrement créé2012-08-15
Enregistrement modifié2020-03-03
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