Failure mode and effect analysis (FMEA) is a powerful reliability management tool for identifying and eliminating known and potential failures in systems, designs, processes, or services to improve their safety and reliability. At present, FMEA has been widely used in various industries. However, the traditional risk priority number (RPN) method has been criticized for many defects. For example, it ignores the relative importance of the risk factors severity (S), occurrence (O), and detection (D), and it is difficult for experts to evaluate the risk of failure modes using precise values from 1 to 10. In this study, we develop a new FMEA model that combines interval type-2 fuzzy sets (IT2FSs) and fuzzy Petri nets (FPNs) to overcome the drawbacks and improve the effectiveness of the traditional FMEA. The rationality and accuracy of the proposed FMEA are illustrated by an example of aerospace electronics manufacturing project. The results show that the new risk assessment model can produce more reliable and reasonable risk ranking results of failure modes in the practical application.
StamatisD.H., Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd ed., ASQ Quality Press, New York, 2003.
2.
BowlesJ.B. and PeláezC.E., Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis, Reliability Engineering & System Safety50 (1995), 203–213.
3.
ShiH., WangL., LiX.Y. and LiuH.C., A novel method for failure mode and effects analysis using fuzzy evidential reasoning and fuzzy Petri nets, Journal of Ambient Intelligence and Humanized Computing (2019), 10.1007/s12652-019-01262-w.
4.
SimseklerM.C.E., KayaG.K., WardJ.R. and ClarksonP.J., Evaluating inputs of failure modes and effects analysis in identifying patient safety risks, International Journal of Health Care Quality Assurance32 (2019), 191–207.
5.
LoH.W., LiouJ.J.H., HuangC.N. and ChuangY.C., A novel failure mode and effect analysis model for machine tool risk analysis, Reliability Engineering and System Safety183 (2019), 173–183.
6.
NieW., LiuW., WuZ., ChenB. and WuL., Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis-technique for order preference by similarity to ideal solution method, Quality and Reliability Engineering International (2019), 10.1002/qre.2468.
7.
LiuH.C., LiP., YouJ.X. and ChenY.Z., A novel approach for FMEA: Combination of interval 2-tuple linguistic variables and grey relational analysis, Quality and Reliability Engineering International31 (2015), 761–772.
8.
LiuH.C.FMEA using Uncertainty Theories and MCDM Methods, Springer, Singapore, 2016.
9.
WangY.M., ChinK.S., PoonG.K.K. and YangJ.B., Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean, Expert Systems with Applications36 (2009), 1195–1207.
10.
LiuH.C., WangL.E., LiZ. and HuY.P., Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method, IEEE Transactions on Fuzzy Systems27 (2019), 84–95.
11.
WangL., HuY.P., LiuH.C. and ShiH., A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development, Quality and Reliability Engineering International (2019), 10.1002/qre.2472.
12.
CarpitellaS., CertaA., IzquierdoJ. and LaC.M., Fata, A combined multi-criteria approach to support FMECA analyses: A real-world case, Reliability Engineering & System Safety169 (2018), 394–402.
13.
CertaA., EneaM., GalanteG.M. and LaC.M., Fata, ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: An alternative to the risk priority number, Computers & Industrial Engineering108 (2017), 100–110.
14.
NieR.X., TianZ.P., WangX.K., WangJ.Q. and WangT.L., Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment, Knowledge-Based Systems162 (2018), 185–201.
15.
ZadehL.A., The concept of a linguistic variable and its application to approximate reasoning–I, Information Sciences8 (1975), 199–249.
16.
TayK.M. and LimC.P., Fuzzy FMEA with a guided rules reduction system for prioritization of failures, International Journal of Quality & Reliability Management23 (2006), 1047–1066.
17.
DağsuyuC.GöçmenE.,
NarlıM.and
KokangülA.Classical and fuzzy FMEA risk analysis in a sterilization unit, Computers & Industrial Engineering101 (2016), 286–294.
18.
BalinA.and BaraçliH., A fuzzy multi-criteria decision making methodology based upon the interval Type-2 fuzzy sets for evaluating renewable energy alternatives in Turkey, Technological and Economic Development of Economy23 (2017), 742–763.
19.
GongY., YangS. and DaiL., Some new signed distances and similarity measures of interval type-2 trapezoidal fuzzy numbers and comparative study, Journal of Intelligent and Fuzzy Systems35 (2018), 3465–3475.
20.
MendelJ.M. and JohnR.I.B., Type-2 fuzzy sets made simple, IEEE Transactions on Fuzzy Systems10 (2002), 117–127.
21.
LiangQ. and MendelJ.M., Interval type-2 fuzzy logic systems: Theory and design, IEEE Transactions on Fuzzy Systems8 (2000), 535–550.
22.
DorfeshanY., MousaviS.M., MohagheghiV. and VahdaniB., Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods, Computers and Industrial Engineering120 (2018), 160–178.
23.
AlikhaniR., TorabiS.A. and AltayN., Strategic supplier selection under sustainability and risk criteria, International Journal of Production Economics208 (2019), 69–82.
24.
EshghiA., MousaviS.M. and MohagheghiV., A new interval type-2 fuzzy approach for analyzing and monitoring the performance of megaprojects based on earned value analysis (with a case study), Neural Computing and Applications (2019), 10.1007/s00521-018-04002-x.
25.
AyodeleT.R., OgunjuyigbeA.S.O., OdigieO. and MundaJ.L., A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria, Applied Energy228 (2018), 1853–1869.
26.
ErginelN., UluskanM., KüçükG. and
AltintaşM., Evaluation methods for completed Six Sigma projects through an interval type-2 fuzzy ANP, Journal of Intelligent and Fuzzy Systems35 (2018), 1851–1863.
27.
ZhouL., WangY. and JiangY., Investment project assessment by a MAGDM method based on the ranking of interval type-2 fuzzy sets, Journal of Intelligent and Fuzzy Systems35 (2018), 1875–1888.
28.
LiuH.C., LiuL. and LinQ.L., Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology, IEEE Transactions on Reliability62 (2013), 23–36.
29.
LiuH.C., LiuL. and LiuN., Risk evaluation approaches in failure mode and effects analysis: A literature review, Expert Systems with Applications40 (2013), 828–838.
30.
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 (2009), 1768–1779.
31.
ChenS.M., KeJ.S. and ChangJ.F., Knowledge representation using fuzzy Petri nets, IEEE Transactions on Knowledge and Data Engineering2 (1990), 311–319.
32.
LooneyC.G., Fuzzy Petri nets for rule-based decision-making, IEEE Transactions on Systems, Man and Cybernetics18 (1988), 178–183.
33.
GaoM.M., ZhouM.C., HuangX.G. and WuZ.M., Fuzzy reasoning Petri nets, IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans33 (2003), 314–324.
34.
LiuH.C., LinQ.L., MaoL.X. and ZhangZ.Y., Dynamic adaptive fuzzy Petri nets for knowledge representation and reasoning, IEEE Transactions on Systems, Man, and Cybernetics: Systems43 (2013), 1399–1410.
35.
SunX.L. and WangN., Gas turbine fault diagnosis using intuitionistic fuzzy fault Petri nets, Journal of Intelligent and Fuzzy Systems34 (2018), 3919–3927.
36.
SunF., ZhangW., ChenJ., WuH., TanC. and SuW., Fused fuzzy Petri nets: A shared control method for Brain Computer Interface systems, IEEE Transactions on Cognitive and Developmental Systems (2018), 10.1109/tcds.2018.2818173.
37.
XuX.G., ShiH., XuD.H. and LiuH.C., Picture fuzzy Petri nets for knowledge representation and acquisition in considering conflicting opinions, Applied Sciences9 (2019), 983.
38.
KimS.Y. and YangY., A self-navigating robot using Fuzzy Petri nets, Robotics and Autonomous Systems101 (2018), 153–165.
39.
ChangY., WuX., ChenG., YeJ., ChenB., XuL., ZhouJ., YinZ. and RenK., Comprehensive risk assessment of deepwater drilling riser using fuzzy Petri net model, Process Safety and Environmental Protection117 (2018), 483–497.
40.
ZhouJ., ReniersG. and ZhangL., A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry, Chemical Engineering Science174 (2017), 136–145.
41.
PuenteJ., PinoR., PrioreP. and de la FuenteD., A decision support system for applying failure mode and effects analysis, International Journal of Quality & Reliability Management19 (2002), 137–150.
42.
BragliaM., FrosoliniM. and MontanariR., Fuzzy criticality assessment model for failure modes and effects analysis, International Journal of Quality & Reliability Management20 (2003), 503–524.
43.
TayK.M. and LimC.P., Enhancing the failure mode and effect analysis methodology with fuzzy inference techniques, Journal of Intelligent & Fuzzy Systems21 (2010), 135–146.
44.
YangZ., BonsallS. and WangJ., Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA, IEEE Transactions on Reliability57 (2008), 517–528.
45.
deJ., Aguiar, R.K. Scalice and D. Bond, Using fuzzy logic to reduce risk uncertainty in failure modes and effects analysis, Journal of the Brazilian Society of Mechanical Sciences and Engineering40 (2018), 516.
46.
IlbaharE., KaraşanA.,
CebiS. and
KahramanC.A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP fuzzy inference system, Safety Science103 (2018), 124–136.
47.
ManglaS.K., LuthraS. and JakharS., Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA: Insights from an Indian case study, Benchmarking25 (2018), 2660–2687.
48.
RenjithV.R., Jose kalathilM.,
KumarP.H. and
MadhavanD., Fuzzy FMECA (failure mode effect and criticality analysis) of LNG storage facility, Journal of Loss Prevention in the Process Industries56 (2018), 537–547.
49.
AhnJ., NohY., ParkS.H., ChoiB.I. and ChangD., Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion, Journal of Power Sources364 (2017), 226–233.
50.
AkyuzE., AkgunI. and CelikM., A fuzzy failure mode and effects approach to analyse concentrated inspection campaigns on board ships, Maritime Policy & Management43 (2016), 887–908.
51.
ChanamoolN. and NaennaT., Fuzzy FMEA application to improve decision-making process in an emergency department, Applied Soft Computing43 (2016), 441–453.
52.
ChengM. and LuY., Developing a risk assessment method for complex pipe jacking construction projects, Automation in Construction58 (2015), 48–59.
53.
WangW., LiuX. and QinY., Multi-attribute group decision making models under interval type-2 fuzzy environment, Knowledge-Based Systems30 (2012), 121–128.
54.
WuD. and MendelJ.M., A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets, Information Sciences179 (2009), 1169–1192.
55.
LiuH.C., XueL., LiZ.W. and WuJ., Linguistic Petri nets based on cloud model theory for knowledge representation and reasoning, IEEE Transactions on Knowledge and Data Engineering30 (2018), 717–728.
56.
YeungD.S. and TsangE.C.C., Weighted fuzzy production rules, Fuzzy Sets and Systems88 (1997), 299–313.
57.
LiuH.C., YouJ.X., YouX.Y. and SuQ., Fuzzy Petri nets using intuitionistic fuzzy sets and ordered weighted averaging operators, IEEE Transactions on Cybernetics46 (2016), 1839–1850.
58.
GuoJ., A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory, Journal of Intelligent & Fuzzy Systems30 (2016), 869–881.
