Failure mode and effects analysis (FMEA) is an extensively used engineering technique for defining, identifying and prioritizing the potential failure modes. Traditionally the Risk Priority Number (RPN) is used to rank the potential failure modes. In this paper, a new risk assessment methodology which combines intuitionistic fuzzy sets (IFSs) with evidence theory is proposed to analyze the potential failure modes. The risk factors S, O, and D are evaluated by using linguistic variables and intuitionistic fuzzy numbers, which can be transformed into the basic probability assignment functions. The Jousselme distance is used to compute the weights of decision makers in order to efficiently combine highly conflicting evidence. The weighted average of evidence is obtained and the classical Dempster’s combination rule is used to combine the mass functions modified. The proposed approach is applied thereafter to the prioritization of failure modes. A numerical example for risk assessment is given to illustrate the effectiveness and rationality of the proposed method finally.
AtanassovK.T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems20(1) (1986), 87–96.
2.
BoranF.E., GencS., KurtM. and AkayD., A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Systems with Applications36(8) (2009), 11363–11368.
3.
BowlesJ.B. and PelaezC.E., Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis, Reliability Engineering and System Safety50(2) (1995), 203–213.
4.
BragliaM., MAFMA: Multi-attribute failure mode analysis, International Journal of Quality & Reliability Management17(9) (2000), 1017–1033.
5.
BragliaM., FrosoliniM. and MontanariR., Fuzzy criticality assessment model for failure modes and effects analysis, International Journal of Quality & Reliability Management20(4) (2003), 503–524.
6.
BragliaM., FrosoliniM. and MontanariR., Fuzzy TOPSIS approach for failure mode, effects and criticality analysis, Quality and Reliability Engineering International19(5) (2003), 425–443.
7.
ChangD.S. and SunK.L.P., Applying DEA to enhance assessment capability of FMEA, International Journal of Quality & Reliability Management26(6) (2009), 629–643.
8.
ChangK.H., Evaluate the orderings of risk for failure problems using a more general RPN methodology, Microelectronics Reliability49(12) (2009), 1586–1596.
9.
ChangK.H. and ChengC.H., A risk assessment methodology using intuitionistic fuzzy set in FMEA, International Journal of Systems Science41(12) (2010), 1457–1471.
10.
ChangK.H., ChengC.H. and ChangY.C., Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique, Soft Computing14(3) (2010), 285–298.
11.
ChangK.H. and ChengC.H., Evaluating the risk of failure using the fuzzy OWA and DEMATEL method, Journal of Intelligent Manufacturing22(2) (2011), 113–129.
12.
ChinK.S., WangY.M., PoonG.K.K. and YangJ.B., Failure mode and effects analysis by data envelopment analysis, Decision Support Systems48(1) (2009), 246–256.
13.
ChinK.S., WangY.M., PoonG.K.K. and YangJ.B., Failure mode and effects analysis using a group-based evidential reasoning approach, Computers & Operations Research36(6) (2009), 1768–1779.
14.
DempsterA., Upper and lower probabilities induced by a multivalued mapping, Annals of Mathematics and Statistics38(2) (1967), 325–339.
15.
DengY., SadiqR., JiangW. and TesfamariamS., Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach, Expert Systems with Applications38(12) (2011), 15438–15446.
16.
DengY., ShiW.K., ZhuZ.F. and LiuQ., Combining belief functions based on distance of evidence, Decision Support Systems38(3) (2004), 489–493.
17.
DymovaL. and SevastjanovP., An interpretation of intuitionistic fuzzy sets in terms of evidence theory: Decision making aspect, Knowledge-based System23(8) (2010), 772–782.
18.
DymovaL. and SevastjanovP., The operations on intuitionistic fuzzy values in the framework of Dempster-Shafer theory, Knowledge-based System35(11) (2012), 132–143.
19.
JousselmeA.L., GrenierD. and BosseE., A new distance between two bodies of evidence, Information Fusion2(2) (2001), 91–101.
20.
LiY., DengY. and KangB., A risk assessment methodology based on evaluation of group intuitionistic fuzzy sets, Journal of Information & Computational Science9(7) (2012), 1855–1862.
21.
LiuH.C., LiuL., BianQ.H., LinQ.L., DongN. and XuP.C., Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory, Expert Systems with Applications38(4) (2011), 4403–4415.
22.
LiuH.C., LiuL. and LiP., Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator, International Journal of Systems Science45(10) (2014), 2012–2030.
23.
LiuH.C., LiuL. and LiuN., Risk evaluation approaches in failure mode and effects analysis: A literature review, Expert Systems with Applications40(2) (2013), 828–838.
24.
LiuH.C., YouJ.X. and YouX.Y., Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure, Computers & Industrial Engineering78 (2014), 249–258.
25.
LiuH.C., YouJ.X., YouX.Y. and ShanM.M., A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method, Applied Soft Computing28 (2015), 579–588.
26.
LiuW.L., Analyzing the degree of conflict among belief functions, Artificial Intelligence170(11) (2006), 909–924.
27.
Mizrak OzfiratP., A new risk analysis methodology integrating fuzzy prioritization method and failure modes and effects analysis, Journal of the Faculty of Engineering and Architecture of Gazi University29(4) (2014), 755–768.
28.
MurphyC.K., Combining belief functions when evidence conflicts, Decision Support Systems29(1) (2000), 1–9.
29.
PillayA. and WangJ., Modified failure mode and effects analysis using approximate reasoning, Reliability Engineering & System Safety79(1) (2003), 69–85.
30.
ShaferG., Mathematical Theory of Evidence, Princeton: Princeton University Press, 1976.
31.
ShiC., ChengY.M. and PanQ., Method to aggregate hybrid preference information based on intuitionistci fuzzy and evidence theory, Control and Decision27(8) (2012), 1163–1168.
32.
SuX.Y., DengY., MahadevanS. and BaoQ.L., An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades, Engineering Failure Analysis26(12) (2012), 164–174.
33.
TayK.M. and LimC.P., Enhancing the failure mode and effect analysis methodology with fuzzy inference techniques, Journal of Intelligent & Fuzzy Systems21(1-2) (2010), 135–146.
34.
VahdaniB., SalimiM. and CharkhchianM., A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process, International Journal of Advanced Manufacturing Technology77(1-4) (2015), 357–368.
35.
WangY. and LeiY.J., A technique for constructing intuitionistic fuzzy entropy, Control and Decision22(12) (2007), 1390–1394.
36.
WangY.M., YangJ.B. and XuD.L., A preference aggregation method through the estimation of utility intervals, Computers and Operations Research32(8) (2005), 2027–2049.
37.
WangY.M., YangJ.B. and XuD.L., A two-stage logarithmic goal programming method for generating weights from interval comparison matrices, Fuzzy Sets and Systems152(3) (2005), 475–498.
38.
XuZ.S., An overview of methods for determining OWA weights, International Journal of Intelligent Systems20(8) (2005), 843–865.
39.
XuZ.S., Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators, Knowledge-Based Systems24(6) (2011), 749–760.
40.
YangJ.P., HuangH.Z., HeL.P., ZhuS.P. and WenD.W., Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty, Engineering Failure Analysis18(8) (2011), 2084–2092.
41.
ZadehL.A., Review of books: A mathematical theory of evidence, AI Magazine5(3) (1984), 81–83.
42.
ZhangS.D. and WangL.D., Absurdities about Dempter–Shafer’s rule of combination of evidence, Systems Engineering–Theory and Practice17(5) (1997), 82–85.
