Download FMEA Using Uncertainty Theories and MCDM Methods by Hu-Chen Liu PDF

By Hu-Chen Liu

This publication deals a radical and systematic advent to the changed failure mode and influence research (FMEA) versions in line with uncertainty theories (e.g. fuzzy common sense, intuitionistic fuzzy units, D numbers and 2-tuple linguistic variables) and numerous multi-criteria determination making (MCDM) methods reminiscent of distance-based MCDM, compromise score MCDM and hybrid MCDM, and so forth. As such, it presents crucial FMEA tools and functional examples that may be thought of in utilising FMEA to augment the reliability and defense of goods and providers. The publication deals a worthy consultant for practitioners and researchers operating within the fields of caliber administration, selection making, details technology, administration technological know-how, engineering, and so forth. it might even be used as a textbook for postgraduate and senior undergraduate students.

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M; j ¼ 1; 2; . ; n; ð3:11Þ k¼1 l   X kk wkj wj ¼ IFWA w1j ; w2j ; . ; wlj ¼ k¼1 " ¼ 1À l  Y # l  k kk Y k k k 1 À lj ; vj ; k¼1 j ¼ 1; 2; . ; n; ð3:12Þ k¼1 À Á where aij ¼ lij ; vij is the group assessment of the l team members for FMi with À Á respect to RFj and wj ¼ lj ; vj is the group subjective weight of RFj of the l team members. Step 2. Determine the subjective weights of risk À factors Á Based on the group subjective weights wj ¼ lj ; vj ; determined by Eq. 12), the subjective weight of risk factor RFj can be normalized using Eq.

Expert Syst Appl 38(1):206–214 Chapter 4 FMEA Using Interval 2-Tuple Hybrid Weighted Distance Measure The interval 2-tuple linguistic representation method (Zhang 2012) is a useful computational model for computing with words, which has the capability of expressing different types of decision makers’ assessment information and has been widely used in many real-world engineering and management problems. Moreover, via this method, decision makers can express their preferences by the use of linguistic term sets with different granularity of uncertainty.

7, we can see that the ranking orders of FM2 and FM7, FM6, and FM8 are the same and FM5 is ranked behind FM1 when the fuzzy FMEA is applied. However, in the proposed method, FM2 and FM6 are successfully distinguished from FM7 and FM8, respectively. In addition, the results of the proposed method show that FM5 has a high priority compared to FM1. The main reasons for these differences may be as follows (Wang et al. 2009; Liu et al. 2013, 2014): (1) The fuzzy if–then rules with the same consequence but different antecedents are unable to be distinguished from one another by using fuzzy FMEA.

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