Soft failure mode and effects analysis using the OWG operator and hesitant fuzzy linguistic term sets

Abstract

Risk assessment is a critical part of reliability engineering and is vital to the success of an enterprise's performance. Traditionally, risk assessment has used the risk priority number (RPN) to evaluate the risk of failure. Unfortunately, the traditional RPN method has several shortcomings-it loses partially valuable information when some data are missing or nonexistent and does not consider the ordered weight between the severity, occurrence, and detection indicators; further, it does not consider the direct and indirect relationships between failure modes and causes of failure, and it has a high duplication rate. To resolve these issues, this paper integrates the ordered weighted geometric (OWG) operator and hesitant fuzzy linguistic term sets to increase the effectiveness of failure mode and effects analysis (FMEA), named soft FMEA. An empirical case of extreme low-k (ELK) dielectric integration is used to illustrate the proposed method and demonstrate its value. Our results show that the soft FMEA method is applicable to real-world situations and constitutes a more general FMEA method.