DescriptionThe study proposes a risk informed Failure Mode Effects and Criticality Analysis (FMECA) method with Monte Carlo simulation to manage safety. The traditional quantitative analysis of FMECA uses
deterministic evaluation of occurrence, severity, and detection to prioritize each failure mode and rank system’s criticality to decide on the risk mitigation strategies. However, without due attention to risks and uncertainties in the relevant parameter’s estimation, analysis could lead to inadequate evaluation and might mislead priorities of various treatment interventions. Ignoring risk factors associated with the specific operating conditions and system specifications could, therefore, result in poor decisions to manage critical failures. This work uses the rail industry as a case study; FMECA parameters of failure rate, occurrence, severity, and detection are considered as safety non-event objectives that may
have a range of probabilistic estimates due to uncertainties. Major contributing risks involving Human and Organizational Factors (HOF) are assessed with a Monte Carlo simulation model to calculate confidence levels of output distribution. This realistic distribution is to inform decisions about the most suitable maintenance regime, rather than the simpler deterministic single values of variables for each failure mode in a standard FMECA. The results of the method introduce a risk informed maintenance improvement that can be translated to other operational areas and roll out to the top of a system hierarchy to support implementation of whole system performance monitoring. The technique
also attempted to address multiple issues confronting the FMECA practical application. The paper illustrates the updated method with the case study of a freight locomotive maintenance.
|Period||2 Mar 2023|
|Event title||3rd International Engineering Conference and Exhibition|
|Location||Riyadh, Saudi ArabiaShow on map|
|Degree of Recognition||International|