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A dynamic risk assessment methodology for maintenance decision support

Journal Contribution - Journal Article

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision making domain. Recent improvements on the FMEA have largely focused on addressing the shortcomings of the conventional FMEA of which, the risk priority number (RPN) is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the RPN metrics, that is, occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data is sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure, of which, the posterior function combines both prior functions elicited from experts, and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which, the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability demonstrated in the case study of thermal power plant equipment failures.
Journal: Quality and Reliability Engineering International
ISSN: 0748-8017
Issue: 3
Volume: 33
Pages: 551 - 563
Publication year:2016
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:1
CSS-citation score:2
Authors:International
Authors from:Higher Education