Aggregation functions for engineering design trade-offs
Fuzzy Sets and Systems
A fusion approach for managing multi-granularity linguistic term sets in decision making
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy modeling of system behavior for risk and reliability analysis
International Journal of Systems Science
Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Group decision making to better respond customer needs in software development
Computers and Industrial Engineering
Integrated framework of risk evaluation and risk allocation with bounded data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: Risk evaluation approaches in failure mode and effects analysis: A literature review
Expert Systems with Applications: An International Journal
Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Failure mode and effects analysis (FMEA) is a powerful tool for identifying and assessing potential failures. The tool has become increasingly important in new product development, manufacture or engineering applications. Generally, risk assessment in FMEA is carried out by using risk priority numbers (RPNs) which can be determined by evaluating three factors: occurrence (O), severity (S) and detection (D). Due to the vagueness and uncertainty existing in the evaluating process, crisp numbers representing RPNs in the traditional FMEA might be improper or insufficient in contrast to fuzzy numbers. Currently, the fuzzy methods and linear programming method have been proposed as an effective solution for the calculations of fuzzy RPNs. However, considering the fact that fuzzy RPNs are determined on a multidimensional scale spanning O, S and D along with their interactions under a fuzzy environment, several gaps should be bridged in the evaluation, calculation, and ranking of fuzzy RPNs. First, decision makers tend to use multi-granularity linguistic term sets for expressing their assessments because of their different backgrounds and preferences. Second, numerical compensation may be existed among O, S and D that can derive different RPNs in the engineering applications. Third, the complete ranking results for fuzzy RPNs may be easily changed by the effects of uncertain factors. In this study, a fuzzy-RPNs-based method integrating weighted least square method, the method of imprecision and partial ranking method is proposed to generate more accurate fuzzy RPNs and ensure to be robust against the uncertainty. A design example of new horizontal directional drilling machine is used for illustrating the application of the proposed approach.