Data mining-based methods for fault isolation with validated FMEA model ranking

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Journal titleApplied Intelligence
SubjectFMEA; Data mining; Data driven models; Binary classifier; Model ranking; Fault isolation; Failure mode; FMEA validation
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. When a failure is predicted or detected, it is expected to identify which component is the root cause or to isolate the fault to a specific contributing component. To efficiently perform fault isolation, we proposed data mining-based methods for fault isolation by using the validated FMEA information to rank data-driven models. However, FMEA, as a standard document, is produced during the design of products or systems. Therefore, FMEA documentation is rarely validated or updated in practice after it was generated. In order to use reliable FMEA information to rank models for fault isolation, it is necessary to validate FMEA before using it. In this paper, we first present brief overview of FMEA validation. Then we introduce the proposed data mining based method for fault isolation. Finally we apply the proposed methods to Auxiliary Power Unit (APU) fault isolation for a given failure mode, “Inability to Start”, by conducting large-scale experiments. The experimental results obtained from a case study demonstrate the usefulness and feasibility of the proposed methods for fault isolation.
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AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21276935
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Record identifiera98e7e93-b9fc-4519-8ecd-08e2698cf3c8
Record created2015-11-10
Record modified2016-05-09
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