Abstract

In view of the shortcomings of traditional failure modes and effects analysis (FMEA) in risk evaluation language, weight information, risk priority number (RPN), this paper proposes an FMEA optimization method. First, using the Pythagorean fuzzy language as the evaluation language, the hesitation psychology of the evaluator is truly reflected. Then, the best worst method (BWM) is used to calculate the weight of the evaluator, it can reduce the number of pairwise comparison evaluations. Second, water filling theory (WFT) uses mean values instead of extreme values to determine the discreteness of evaluation information, which is more consistent with FMEA. Therefore, WFT is used to calculate the weight of influencing factors. Finally, the tomada de-decisao iterativa multicriterio (TODIM) method is used for compromise calculation to obtain the risk ranking of failure modes. Compared with RPN, TODIM can avoid the situation that the failure mode scores are the same. At the end of the paper, the robustness and superiority of the new method are verified by taking the reliability assessment of reversing system of industrial robots as an example.

References

1.
Borujerd
,
S. V.
N.,
Soleimani
,
A.
,
Esfandyari
,
M. J.
,
Masih-Tehrani
,
M.
,
Esfahanian
,
M.
,
Nehzati
,
H.
, and
Dolatkhah
,
M.
,
2023
, “
Fuzzy Logic Approach for Failure Analysis of Li-Ion Battery Pack in Electric Vehicles
,”
Eng. Failure Anal.
,
149
, p.
107233
.10.1016/j.engfailanal.2023.107233
2.
Huang
,
W.
, and
Zhang
,
Y.
,
2021
, “
Railway Dangerous Goods Transportation System Risk Assessment: An Approach Combining FMEA With Pessimistic-Optimistic Fuzzy Information Axiom Considering Acceptable Risk Coefficient
,”
IEEE Trans. Reliab.
,
70
(
1
), pp.
371
388
.10.1109/TR.2020.2973431
3.
Minguito
,
G.
, and
Banluta
,
J.
,
2023
, “
Risk Management in Humanitarian Supply Chain Based on FMEA and Grey Relational Analysis
,”
Socio-Econ. Plann. Sci.
,
87
, p.
101551
.10.1016/j.seps.2023.101551
4.
Li
,
Y.
,
Liu
,
P.
, and
Li
,
G.
,
2023
, “
An Asymmetric Cost Consensus Based Failure Mode and Effect Analysis Method With Personalized Risk Attitude Information
,”
Reliab. Eng. Syst. Saf.
,
235
, p.
109196
.10.1016/j.ress.2023.109196
5.
Zakeri
,
S.
,
Konstantas
,
D.
,
Bratvold
,
R. B.
, and
Pamucar
,
D.
,
2023
, “
A Supplier Selection Model Using the Triangular Fuzzy-Grey Numbers
,”
IEEE Access
,
11
, pp.
107511
107532
.10.1109/ACCESS.2023.3320032
6.
Liu
,
G.
, and
Wang
,
X.
,
2023
, “
A Trapezoidal Fuzzy Number-Based VIKOR Method With Completely Unknown Weight Information
,”
Symmetry (Basel)
,
15
(
2
), p.
559
.10.3390/sym15020559
7.
Nguyen
,
T. T.
,
2016
, “
Portfolio Selection Under Higher Moments Using Fuzzy Multi-Objective Linear Programming
,”
J. Intell. Fuzzy Syst.
,
30
(
4
), pp.
2139
2156
.10.3233/IFS-151927
8.
Liu
,
J.
, and
Wang
,
L.
,
2023
, “
Hybrid Reliability-Based Sequential Optimization for PID Vibratory Controller Design Considering Interval and Fuzzy Mixed Uncertainties
,”
Appl. Math. Modell.
,
122
, pp.
796
823
.10.1016/j.apm.2023.05.022
9.
Sahebi
,
I. G.
,
Toufighi
,
S. P.
,
Karakaya
,
G.
, and
Ghorbani
,
S.
,
2022
, “
An Intuitive Fuzzy Approach for Evaluating Financial Resiliency of Supply Chain
,”
Opsearch
,
59
(
2
), pp.
460
481
.10.1007/s12597-021-00563-z
10.
Aarthi
,
S.
, and
Shanmugasundari
,
M.
,
2023
, “
Comparison of Single Server Queuing Performance Measures Using Fuzzy Queuing Models and Intuitionistic Fuzzy Queuing Models With Infinite Capacity
,”
J. Intell. Fuzzy Syst.
,
44
(
3
), pp.
4733
4746
.10.3233/JIFS-221367
11.
Pang
,
J.
,
Dai
,
J.
, and
Li
,
Y.
,
2022
, “
An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory
,”
Mathematics (Basel
),
10
(
9
), p.
1437
.10.3390/math10091437
12.
Khan
,
M. J.
,
Alcantud
,
J. C. R.
,
Kumam
,
W.
,
Kumam
,
P.
, and
Alreshidi
,
N. A.
,
2023
, “
Expanding Pythagorean Fuzzy Sets With Distinctive Radii: Disc Pythagorean Fuzzy Sets
,”
Complex Intell. Syst.
,
9
(
6
), pp.
7037
7054
.10.1007/s40747-023-01062-y
13.
Nguyen
,
T. Q.
,
Doan
,
Q. M.
, and
Ngo
,
L. T. T.
,
2024
, “
An Assessment of Factors for the Cruise Port of Call Selection: The Modified Fuzzy Analytic Hierarchy Process
,”
PLoS One
,
19
(
2
), p.
e0297293
.10.1371/journal.pone.0297293
14.
Balakrishnan
,
N.
,
Paroissin
,
C.
, and
Turlot
,
J.
,
2015
, “
One-Sided Control Charts Based on Precedence and Weighted Precedence Statistics
,”
Qual. Reliab. Eng. Int.
,
31
(
1
), pp.
113
134
.10.1002/qre.1750
15.
Danacı
,
M.
, and
Yıldırım
,
U.
,
2023
, “
Comprehensive Analysis of Lifeboat Accidents Using the Fuzzy Delphi Method
,”
Ocean Eng.
,
278
, p.
114371
.10.1016/j.oceaneng.2023.114371
16.
Li
,
Z.
,
Wang
,
L.
, and
Geng
,
X.
,
2024
, “
A Level Set Reliability-Based Topology Optimization (LS-RBTO) Method Considering Sensitivity Mapping and Multi-Source Interval Uncertainties
,”
Comput. Methods Appl. M
ech. Eng.,
419
, p.
116587
.10.1016/j.cma.2023.116587
17.
Xiang
,
C.
,
Xiao Qin
,
J.
, and
Yin
,
L.
,
2020
, “
Study on the Rural Ecotourism Resource Evaluation System
,”
Environ. Technol. Innovation
,
20
, p.
101131
.10.1016/j.eti.2020.101131
18.
Akram
,
M.
,
Ramzan
,
N.
, and
Deveci
,
M.
,
2023
, “
Linguistic Pythagorean Fuzzy CRITIC-EDAS Method for Multiple-Attribute Group Decision Analysis
,”
Eng. Appl. Artif. Intell.
,
119
, p.
105777
.10.1016/j.engappai.2022.105777
19.
Dorabiala
,
O.
,
Aravkin
,
A. Y.
, and
Kutz
,
J. N.
,
2024
, “
Ensemble Principal Component Analysis
,”
IEEE Access
,
12
, pp.
6663
6671
.10.1109/ACCESS.2024.3350984
20.
Pandey
,
J.
,
Ansari
,
M. Z.
, and
Husain
,
A.
,
2024
, “
Experimental and Numerical Investigation for Hydrothermal Performance of the Jet Impingement Microchannel Heat Sink
,”
ASME J. Heat Mass Transfer-Trans. ASME
,
146
(
7
), p.
072302
.10.1115/1.4065454
21.
Mei
,
X.
, and
Wu
,
K.
,
2022
, “
Capacity-Oriented Envelope Correlation Coefficient for Multiple Antennas of Mobile Devices
,”
IEEE Trans. Antennas Propag.
,
70
(
10
), pp.
9785
9794
.10.1109/TAP.2022.3184541
22.
Ciardiello
,
F.
, and
Genovese
,
A.
,
2023
, “
A Comparison Between TOPSIS and SAW Methods
,”
Ann. Oper. Res.
,
325
(
2
), pp.
967
994
.10.1007/s10479-023-05339-w
23.
Ramayee
,
L.
,
Supradeepan
,
K.
,
Sreejith
,
S.
, and
Priyadarshini
,
A.
,
2022
, “
Multi-Objective Optimization of Lobed Enclosure for Wind Turbine Applications Using Gray Relation Analysis
,”
ASME J. Energy Resour. Technol.
,
144
(
11
), p.
111302
.10.1115/1.4054431
24.
Xu
,
X.
,
Yu
,
F.
,
Pedrycz
,
W.
, and
Du
,
X.
,
2023
, “
Multi-Source Fuzzy Comprehensive Evaluation
,”
Appl. Soft Comput.
,
135
, p.
110042
.10.1016/j.asoc.2023.110042
25.
Tsionas
,
M. G.
,
2023
, “
Clustering and Meta-Envelopment in Data Envelopment Analysis
,”
Eur. J. Oper. Res.
,
304
(
2
), pp.
763
778
.10.1016/j.ejor.2022.04.015
26.
Liu
,
Y.
,
Wang
,
L.
, and
Ng
,
B.
,
2024
, “
A Hybrid Model-Data-Driven Framework for Inverse Load Identification of Interval Structures Based on Physics-Informed Neural Network and Improved Kalman Filter Algorithm
,”
Appl. Energ
y,
359
, p.
122740
.10.1016/j.apenergy.2024.122740
27.
Zhang
,
Z.
,
Guo
,
J.
,
Zhang
,
H.
,
Zhou
,
L.
, and
Wang
,
M.
,
2022
, “
Product Selection Based on Sentiment Analysis of Online Reviews: An Intuitionistic Fuzzy TODIM Method
,”
Complex Intell. Syst.
,
8
(
4
), pp.
3349
3362
.10.1007/s40747-022-00678-w
28.
Theerens
,
A.
, and
Cornelis
,
C.
,
2023
, “
Fuzzy Rough Sets Based on Fuzzy Quantification
,”
Fuzzy Sets Syst.
,
473
, p.
108704
.10.1016/j.fss.2023.108704
29.
Zhao
,
J.
,
Wan
,
R.
, and
Miao
,
D.
,
2024
, “
Conflict Analysis Triggered by Three-Way Decision and Pythagorean Fuzzy Rough Set
,”
Int. J. Comput. Intell. Sys
t.,
17
(
1
), p.
17
.10.1007/s44196-023-00378-4
30.
Wan
,
S.
,
Dong
,
J.
, and
Chen
,
S.
,
2021
, “
Fuzzy Best-Worst Method Based on Generalized Interval-Valued Trapezoidal Fuzzy Numbers for Multi-Criteria Decision-Making
,”
Inf. Sci.
,
573
, pp.
493
518
.10.1016/j.ins.2021.03.038
31.
Rezaei
,
J.
,
2016
, “
Best-Worst Multi-Criteria Decision-Making Method: Some Properties and a Linear Model
,”
Omega
,
64
, pp.
126
130
.10.1016/j.omega.2015.12.001
32.
Hu
,
J.
,
Wang
,
Q.
,
Zhang
,
Y.
,
Meng
,
Z.
,
Zhang
,
J.
, and
Fan
,
J.
,
2023
, “
Numerical and Experimental Study on the Process of Filling Water in Pressurized Water Pipeline
,”
Water
,
15
(
14
), p.
2508
.10.3390/w15142508
33.
Wu
,
X.
,
Zhu
,
Z.
,
Chen
,
G.
,
Pedrycz
,
W.
,
Liu
,
L.
, and
Aggarwal
,
M.
,
2024
, “
Generalized TODIM Method Based on Symmetric Intuitionistic Fuzzy Jensen–Shannon Divergence
,”
Expert Syst. Appl.
,
237
, p.
121554
.10.1016/j.eswa.2023.121554
34.
Song
,
C.
,
Xu
,
Z.
,
Zhang
,
Y.
, and
Li
,
B.
,
2023
, “
Environmental Quality Evaluation Based on the TODIM Method With Normal Wiggly Hesitant Fuzzy Set
,”
Soft Comput.
,
27
(
12
), pp.
8161
8173
.10.1007/s00500-023-08155-3
35.
Pang
,
J.
,
Dai
,
J.
, and
Qi
,
F.
,
2021
, “
A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems
,”
Math. Probl. Eng.
,
2021
, p.
9998526
.10.1155/2021/9998526
36.
Taylor
,
P. A.
,
Miles
,
E.
,
Hoffmann
,
L.
,
Kelly
,
S. M.
,
Kry
,
S. F.
,
Sloth Møller
,
D.
, and
Palmans
,
H.
, et al.
,
2023
, “
Prioritizing Clinical Trial Quality Assurance for Photons and Protons: A Failure Modes and Effects Analysis (FMEA) Comparison
,”
Radiother. Oncol.
,
182
, p.
109494
.10.1016/j.radonc.2023.109494
37.
Li
,
W.
,
Dong
,
F.
,
Shi
,
M.
,
Wang
,
X.
, and
Ji
,
Z.
,
2024
, “
Multi-Attribute Decision-Making Research on Investment Suitability Assessment of Hydropower-Wind-Photovoltaic-Storage Complementary System Based on Dynamic Social Network
,”
Energy Convers. Manage.
,
307
, p.
118358
.10.1016/j.enconman.2024.118358
38.
Wang
,
Y.
,
Liu
,
P.
, and
Yao
,
Y.
,
2022
, “
BMW-TOPSIS: A Generalized TOPSIS Model Based on Three-Way Decision
,”
Inf. Sci.
,
607
, pp.
799
818
.10.1016/j.ins.2022.06.018
39.
Alhadidi
,
T. I.
, and
Alomari
,
A. H.
,
2024
, “
A FAHP-VIKOR Model for Evaluating Single Point Interchange Operational Performance
,”
Expert Syst. Appl.
,
248
, p.
123386
.10.1016/j.eswa.2024.123386
40.
Jahan
,
A.
,
Ismail
,
M. Y.
,
Shuib
,
S.
,
Norfazidah
,
D.
, and
Edwards
,
K. L.
,
2011
, “
An Aggregation Technique for Optimal Decision-Making in Materials Selection
,”
Mater. Des.
,
32
(
10
), pp.
4918
4924
.10.1016/j.matdes.2011.05.050
You do not currently have access to this content.