Data mining based fault isolation with FMEA rank: A case study of APU fault identification

DOIResolve DOI: http://doi.org/10.1109/ICPHM.2013.6621454
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TypeArticle
Proceedings titlePHM 2013 - 2013 IEEE International Conference on Prognostics and Health Management, Conference Proceedings
Conference2013 IEEE International Conference on Prognostics and Health Management, PHM 2013, 24 June 2013 through 27 June 2013, Gaithersburg, MD
ISBN9781467357227
Article number6621454
AbstractFMEA (Failure Mode and Effects Analysis), which was developed to enhance the reliability of complex systems, is a standard method to characterize and document product and process problems and a systematic method for fault identification/isolation in maintenance industry. Fault identification for a given failure effect or mode is a reactive process. Usually, a failure has occurred and it needs to identify which component is the root cause or to isolate the fault to a specific contributing component. Traditional method is to conduct TSM (Trouble Shooting Manuals)-based fault isolation, which is complicated, expensive, and time-consuming. To efficiently perform fault isolation, this paper proposed data mining-based framework for fault isolation by using FMEA information to rank data-driven models. In this paper, we present the proposed framework along with a case study for APU fau It identification. © 2013 IEEE.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Information and Communication Technologies (ICT-TIC)
Peer reviewedYes
NPARC number21269983
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Record identifier507d51da-6ccb-4e5b-8a8c-efe05b65175f
Record created2013-12-13
Record modified2016-05-09
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