MODIFICATION OF THE FMEA METHOD USING MACHINE LEARNING ALGORITHMS

Abstract

Risk assessment is an important task in any field, from manufacturing to medicine. Risks accompany a project, product or process throughout its life, from the moment of planning until its complete termination. Each of them has its own approaches. These include FMEA (Failure Mode and Effects Analysis) - analysis of the types and consequences of failures. The proposed model is based on the FMEA method, which is based on risk assessment according to three criteria: the severity of the consequences when a threat is realized and the complexity of identifying a failure, the probability of occurrence. The first two criteria are based on expert assessment obtained in accordance with artificial intelligence methods. The authors proposed a modification of the third criterion. In our work, we replaced the expert assessment of the “probability of occurrence” criterion with a machine learning model capable of predicting this indicator based on statistical data. We carried out the first stage of research into the task at hand on NASA’s open dataset about engine operating cycles before failure. Initially, the task was set to predict the remaining number of cycles before failure, then we moved to the classification task, determining whether the equipment is at risk, depending on its potential remaining life. The best result was obtained by the support vector machine (SVM), with a classification accuracy of 80%. The goal of the work is to create a risk assessment model based on the FMEA methodology, which allows to improve the quality of assessment, reduce subjectivity in decision making, making a forecast based on historical data, and not just the subjective experience of an expert.