| Download | - View accepted manuscript: Improving Preciseness of Time to Failure Predictions: Application to APU starter (PDF, 1.1 MiB)
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| Author | Search for: Létourneau, Sylvain; Search for: Yang, Chunsheng; Search for: Liu, Ziying |
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| Format | Text, Article |
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| Conference | First International Conference on Prognostics and Health Management (PHM 2008), October 6-9, 2008, Denver, Colorado, USA |
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| Abstract | Despite 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. |
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| Publication date | 2008 |
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| In | |
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| Language | English |
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| NRC number | NRCC 50409 |
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| NPARC number | 8913204 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 5f28f850-5b03-46ba-a475-05e6fc812626 |
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| Record created | 2009-04-22 |
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| Record modified | 2020-08-12 |
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