Risk Evaluation: Brief Review and Innovation Model Based on Fuzzy Logic and MCDM

The risk assessment of engineering systems represents an important part the quality service and dependability. existing methods for evaluation use crisp sets rating partial indicators’ proposition their cumulative products as overall indicator. In this paper, FMEA FMECA have been improved using fuzzy expert system calculating priority number. application logic allows linguistic descriptions analysis. way, state in terms risks consequences is better described. settings are based on two multi-criteria decision-making methods. AHP method was used to define mutual relationship impact indicators (occurrence, severity, detectability) risk. subjectivity reduced. composition model, TOPSIS introduced reduce dissipation results, which contributes accuracy outcome. This results. results were verified through a case study complex system—bucket-wheel excavators. observed from aspect danger damage downtime. initial information weak points ES defined according historical events statistics Expert knowledge grading model. Additional model verification performed similar methods, same input data. innovative presented shows that it possible correct different weights indicators. obtained show less dispersion compared with other Weak increased located, algorithm has proposed risk-based maintenance implementation.