In this paper, an alternative multi-attribute decision-making approach for prioritizing failures in failure mode, effects and criticality analysis (FMECA) is presented. The technique is specifically intended to overcome some of the limitations concerning the use of the conventional US MIL-STD-1629A method. The approach is based on a fuzzy version of the 'technique for order preference by similarity to ideal solution' (TOPSIS). The use of fuzzy logic theory allows one to avoid the intrinsic difficulty encountered in assessing 'crisp' values in terms of the three FMECA parameters, namely chance of failure, chance of non-detection, and severity. With the proposed approach, the definition of a knowledge base supported by several qualitative rule bases is no longer required. To solve the fundamental question of ranking the final fuzzy criticality value, a particular method of classification is adopted, allowing a fast and efficient sorting of the final outcome. An application to an important Italian domestic appliance manufacturer and a comparison with conventional FMECA are reported to demonstrate the characteristics of the proposed method. Finally, a sensitivity analysis of the fuzzy judgement weights has confirmed that the proposed approach gives a reasonable and robust final priority ranking of the different causes of failure.
Fuzzy TOPSIS approach for failure mode, effects and criticality analysis
BRAGLIA, MARCELLO;FROSOLINI, MARCO;
2003-01-01
Abstract
In this paper, an alternative multi-attribute decision-making approach for prioritizing failures in failure mode, effects and criticality analysis (FMECA) is presented. The technique is specifically intended to overcome some of the limitations concerning the use of the conventional US MIL-STD-1629A method. The approach is based on a fuzzy version of the 'technique for order preference by similarity to ideal solution' (TOPSIS). The use of fuzzy logic theory allows one to avoid the intrinsic difficulty encountered in assessing 'crisp' values in terms of the three FMECA parameters, namely chance of failure, chance of non-detection, and severity. With the proposed approach, the definition of a knowledge base supported by several qualitative rule bases is no longer required. To solve the fundamental question of ranking the final fuzzy criticality value, a particular method of classification is adopted, allowing a fast and efficient sorting of the final outcome. An application to an important Italian domestic appliance manufacturer and a comparison with conventional FMECA are reported to demonstrate the characteristics of the proposed method. Finally, a sensitivity analysis of the fuzzy judgement weights has confirmed that the proposed approach gives a reasonable and robust final priority ranking of the different causes of failure.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
