A New Integrated Approach for Risk Evaluation and Classification With Dynamic Expert Weights

Abstract

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach.