FAME-X: Failure Mechanism Identification Expert System

Dimitrios Karampelas

    Research output: ThesisEngD ThesisAcademic

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    Although maintenance tries to prevent them, failures regularly occur in practice. Prevention prerequisites insight on the “how” a failure occurs. Failure mechanisms (FM) describe how components fail. However, operators or technicians as the first people to deal with a failure, normally lack the knowledge to access FM that act on a material level. Current failure reporting is typically done by entering a failure type/failure mode/cause manually or selecting from a drop-down menu. However, this process has been reported as problematic by several industries. Thus, this study proposes the use of an Expert System (ES) as an app in mobile devices to assist operators in assessing the appropriate FM. The basic idea behind the ES is simply that failure analysis expertise is transferred from a human to a computer in the form of heuristics. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed. The computer asks questions regarding failure diagnostic characteristics, makes inferences and arrives at a specific conclusion. Then, like a human consultant, it gives advices on the most probable FM and explains, if necessary, the logic behind the advice. The ES is based on an extensive study of failure diagnostic characteristics and their contribution to FM. In contrast to failure modes, the proposed FM approach enhances the decision making upon proper remedial actions. The ES can help standardize failure mechanism identification for a given set of characteristics, unify failure terminology, and codify and document the failure investigation procedure.
    Original languageEnglish
    Awarding Institution
    • University of Twente
    • Tinga, Tiedo, Supervisor
    • Loendersloot, Richard, Co-Supervisor
    Award date14 Mar 2018
    Place of PublicationEnschede
    Print ISBNs978-90-365-4517-4
    Publication statusPublished - 14 Mar 2018

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