FAME-X: Failure Mechanism Identification Expert System

Dimitrios Karampelas

Research output: ThesisPd Eng ThesisAcademic

115 Downloads (Pure)

Abstract

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
Supervisors/Advisors
  • Tinga, Tiedo , Supervisor
  • Loendersloot, Richard , Co-Supervisor
Award date14 Mar 2018
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4517-4
DOIs
Publication statusPublished - 14 Mar 2018

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Expert systems
Failure modes
Terminology
Application programs
Mobile devices
Failure analysis
Decision making
Industry

Cite this

Karampelas, Dimitrios . / FAME-X : Failure Mechanism Identification Expert System. Enschede : University of Twente, 2018. 110 p.
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abstract = "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.",
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FAME-X : Failure Mechanism Identification Expert System. / Karampelas, Dimitrios .

Enschede : University of Twente, 2018. 110 p.

Research output: ThesisPd Eng ThesisAcademic

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T1 - FAME-X

T2 - Failure Mechanism Identification Expert System

AU - Karampelas, Dimitrios

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N2 - 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.

AB - 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.

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