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

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DOIResolve DOI: http://doi.org/10.1007/978-3-642-21822-4_18
AuthorSearch for: ; Search for:
TypeBook Chapter
Proceedings titleModern 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
Series titleLecture Notes In Computer Science; Volume 6703
Conference24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2011), June 28-July 1, 2011, Syracuse, NY, USA
ISSN0302-9743
ISBN978-3-642-21821-7
978-3-642-21822-4
Pages165175; # of pages: 11
Subjectdata mining; time-series; reliable patterns; utility; prognostics
AbstractData-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.
Publication date
PublisherSpringer Berlin Heidelberg
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number20494949
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Record identifier95667e55-10fe-4ad4-83ba-80ba3613cdc2
Record created2012-08-15
Record modified2016-06-21
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