A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment

Abstract

Failure mode and effects analysis (FMEA) is a systematic and proactive risk prevention and management tool used to improve the reliability and safety of a system, process or service. However, many defects around the traditional FMEA affect the effectiveness of its practical applications. When dealing with the FMEA problems, the first and the most important step is to collect the risk assessment information of experts regarding failure modes. Moreover, clustering failure modes is more adaptable to actual needs, especially under a tight resource constraint. In this article, a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering is developed to evaluate and cluster the risk of failure modes. First, the DHHFLTSs are utilized to describe the complex linguistic assessments on failure modes given by FMEA team members. Next, an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means algorithm is proposed and used to cluster failure modes into priority classes. Finally, a case study concerning the risk evaluation over floating offshore wind turbine is provided to illustrate the effectiveness and usefulness of our proposed FMEA. The results show that the new risk classification approach is more reasonable and practical for risk management decision making.