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

Abstract

FMEA (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 fault identification.