Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model

In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should be considered for achieving a holistic approach towards mitigating failure modes. This paper proposes a systematic approach for developing a standard equation for RPN measure, using the methodology of interval number based logistic regression. Instead of utilizing RPN in product form for each failure, this method is benefited from decisions based on probability of risk of failure, P' ' which is more realistic in practical applications. A case study is presented to illustrate the application of the proposed methodology in finding the risk of failure of high capacity submersible pumps in the power plant. The developed logistic regression model (logit model) using R software helped in generating the probability of risk of failure equation for predicting the failures. The model showed the correct classification rate to be 77.47%. The Receiver Operating Characteristic (ROC) curve showed the logit-model to be 81.98% accurate with an optimal cut-off value of 0.56.