Improved TOPSIS and CRITIC Methods for Failure Mode and Effects Analysis Based on Z-Number Theory

Failure mode and effects analysis (FMEA), as a means of identifying, preventing, and controlling potential system failures, has been widely applied in many industries. However, the classic FMEA method has several drawbacks in practical applications, such as the uncertainty and ambiguity in the expression of evaluation information, the neglect of the reliability of evaluation information, and the failure to consider the psychological behavior of experts, which leads to inaccurate evaluation results. Therefore, this paper proposes an improved FMEA framework that integrates Z-number theory with the criteria importance through intercriteria correlation (CRITIC) and technique for order preference by similarity to ideal solution (TOPSIS) methods to improve the accuracy of failure mode risk ranking. Specifically, Z-numbers are employed to represent expert assessment information, effectively capturing both fuzziness and reliability. The CRITIC method is then extended with Z-numbers to determine the weights of risk factors, which not only accounts for the interrelationships among factors but also prevents information loss caused by defuzzification of weights. Moreover, to handle missing assessment data, a generalized Z-number distance measure is introduced, and the TOPSIS model is enhanced to rank failure modes by considering the reliability and uncertainty of the information. Finally, the effectiveness of the proposed method is verified by taking the pallet exchange device of a CNC machine tool as an example. The results show that, compared with other FMEA methods, the proposed method considers the reliability and fuzziness of the evaluation information, maintains the original Z-number information structure, and provides more accurate and reliable risk-ranking results.