A novel failure mode and effect analysis method using a flexible knowledge acquisition framework based on picture fuzzy sets

Failure mode and effect analysis (FMEA) is an effective reliability management tool for identifying potential failures in a system/component that has been widely utilized in various fields by combining fuzzy sets theory. However, the difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors are prominent challenges in the existing fuzzy FMEA methods. Thus, this work employs the picture fuzzy sets (PFSs) theory to meet these challenges, allowing experts to express assessments more efficiently and accurately. Meanwhile, we propose a novel FMEA method to improve the three essential processes of FMEA, involving the following steps. Firstly, a flexible knowledge acquisition framework (FKAF) is established to simplify the expert evaluation process, allowing experts to express fuzzy information with various forms of non-fuzzy assessments. Then, a strategy-based picture fuzzy conversion (PFC) method is developed to standardize the non-fuzzy values to picture fuzzy numbers (PFNs). Secondly, to improve the assessment aggregation accuracy in uncertain environments, we suggest the picture fuzzy evidential reasoning (PFER) method that extend the existing fuzzy evidential reasoning (FER) methods. Thirdly, to completely describe the parallel and causal relationships among risk factors, four alternative models are established using picture fuzzy Petri nets (PFPNs) and risk priority rankings are determined through inference. Finally, the effectiveness and superiority of the proposed FMEA method are verified through two case studies and one extended experiment, demonstrating that it overcomes the shortcomings of the existing FMEA methods and reduces application costs while ensuring the rationality of rankings.

Automated summary

This study proposes a novel FMEA method that utilizes picture fuzzy sets theory to address the challenges in existing methods, such as difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors. The method simplifies the expert evaluation process through a flexible knowledge acquisition framework, standardizes non-fuzzy values using a strategy-based picture fuzzy conversion method, improves assessment aggregation accuracy with a picture fuzzy evidential reasoning method, and establishes alternative models using picture fuzzy Petri nets to describe the relationships among risk factors.