Improving Preciseness of Time to Failure Predictions: Application to APU starter

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TypeArticle
ConferenceProceedings of the 1st International Conference on Prognostics and Health Management (PHM 2008), October 6-9, 2008., Denver, Colorado, USA
AbstractDespite the availability of huge amounts of data and a variety of powerful data analysis methods, prognostic models are still often failing to provide accurate and precise time to failure estimations. This paper addresses this problem by integrating several machine learning algorithms. The approach proposed relies on a classification system to determine the likelihoodof component failures and to provide rough indications of remaining life. It then introduces clustering and SVM-based local regression to refine the time to failure estimations provided by the classification system. The paper illustrates the applicability of the proposed approach through a real world aerospace application and discusses data pre-processing requirements. The preliminary results show that the proposed method can reduce uncertainty in time to failure estimates, which in turn helps augment the usefulness of prognostics.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number50409
NPARC number8913204
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Record identifier5f28f850-5b03-46ba-a475-05e6fc812626
Record created2009-04-22
Record modified2016-05-09
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