Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method

TLDR

Failure mode and effect analysis (FMEA) is a widely used prospective reliability technique, but conventional FMEA suffers from drawbacks that limit its effectiveness. This study proposes an integrated FMEA model that combines cloud model theory with hierarchical TOPSIS to assess and rank failure‑mode risks. The model transforms linguistic assessments into normal clouds, derives team‑member weights from subjective data, and applies cloud hierarchical TOPSIS to compute risk priorities. The new method merges the cloud model’s handling of fuzziness with hierarchical TOPSIS’s decision‑making strengths, and empirical examples demonstrate its feasibility and superiority over existing approaches.

Abstract

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.