Abstract:
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 focussed on addressing the
shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure
modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the
risk priority number 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 are 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 is demonstrated in the case study of thermal power plant equipment failures.