New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence

doi:10.23919/JSEE.2024.000124

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (6): 1491-1506.doi: 10.23919/JSEE.2024.000124

• SYSTEMS ENGINEERING • Previous Articles

New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence

Jian WANG¹(), Jingyi ZHU¹(), Hua SHI²^,*(), Huchen LIU³()

¹ School of Management, Shanghai University, Shanghai 200444, China
² School of Materials, Shanghai Dianji University, Shanghai 201306, China
³ School of Economics and Management, Tongji University, Shanghai 200092, China

Received:2023-07-12 Online:2024-12-18 Published:2025-01-14
Contact: Hua SHI E-mail:jwang@t.shu.edu.cn;hnkfzjy@163.com;shihuatongji@sina.com;huchenliu@tongji.edu.cn
About author:
WANG Jian was born in 1974. He received his Ph.D. degree in industrial engineering and management from Tokyo Institute of Technology, Tokyo, Japan, in 2005. He is currently a professor in management science and engineering in Shanghai University, China. His research interests include quality management, manufacturing management, and R&D management. E-mail: jwang@t.shu.edu.cn

ZHU Jingyi was born in 1999. She received her B.S. degree in business school from Henan University, Kaifeng, China in 2017. She is pursuing her M.S. degree in management science and engineering from Shanghai University. Her research interests include failure mode and effects analysis, group consensus and group decision making. E-mail: hnkfzjy@163.com

SHI Hua was born in 1980. He received his M.S. and Ph.D. degrees in management science and engineering from Shanghai University, Shanghai, China, in 2017 and 2020 respectively. He is currently an associate professor with the School of Materials, Shanghai Dianji University, Shanghai, China. His research interests include quality management, artificial intelligence and uncertain decision-making. E-mail: shihuatongji@sina.com

LIU Huchen was born in 1984. He received his M.S. degree in industrial engineering from Tongji University, Shanghai, China, in 2010, and Ph.D. degree in industrial engineering and management from Tokyo Institute of Technology, Tokyo, Japan, in 2013. He is currently a professor at the School of Economics and Management, Tongji University. His research interests include quality engineering, reliability management, and Quality 4.0. E-mail: huchenliu@tongji.edu.cn
Supported by:
This work was supported by the Fundamental Research Funds for the Central Universities (22120240094) and Humanities and Social Science Fund of Ministry of Education China(22YJA630082).

Abstract

Abstract:

Failure mode and effect analysis (FMEA) is a preventative risk evaluation method used to evaluate and eliminate failure modes within a system. However, the traditional FMEA method exhibits many deficiencies that pose challenges in practical applications. To improve the conventional FMEA, many modified FMEA models have been suggested. However, the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes. In this research, we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clustering algorithm for the assessment and clustering of failure modes. Firstly, we employ the interval 2-tuple linguistic variables (I2TLVs) to express the uncertain risk evaluations provided by FMEA experts. Then, a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus. Next, failure modes are categorized into several risk clusters using a density peak clustering algorithm. Finally, the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems. The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs; the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching; and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.

Key words: failure mode and effect analysis (FMEA), interval 2-tuple linguistic variable (I2TLV), consensus reaching, density peak clustering algorithm

Jian WANG, Jingyi ZHU, Hua SHI, Huchen LIU. New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence[J]. Journal of Systems Engineering and Electronics, 2024, 35(6): 1491-1506.

Figures/Tables 13

Table 1

Fig 1

Table 2

Interval 2-tuple risk evaluation matrix given by the first expert"

${F_i}$	${{\mathrm{TM}}_1}$
${F_i}$	${\mathrm{S}}$	${\mathrm{O}}$	${\mathrm{D}}$
${F_1}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$
${F_2}$	$\left[ {\left( {{s_3},0} \right),\left( {{s_4},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_3}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_4},0} \right)} \right]$
${F_4}$	$\left[ {\left( {{s_4},0} \right),\left( {{s_4},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_4},0} \right)} \right]$
${F_5}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_6}$	$\left[ {\left( {{s_3},0} \right),\left( {{s_4},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_7}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_8}$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_9}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{10}}$	$\left[ {\left( {{s_4},0} \right),\left( {{s_4},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{11}}$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{12}}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{13}}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{14}}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{15}}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$
${F_{16}}$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_1},0} \right)} \right]$	$\left[ {\left( {{s_3},0} \right),\left( {{s_4},0} \right)} \right]$
${F_{17}}$	$\left[ {\left( {{s_3},0} \right),\left( {{s_3},0} \right)} \right]$	$\left[ {\left( {{s_1},0} \right),\left( {{s_2},0} \right)} \right]$	$\left[ {\left( {{s_2},0} \right),\left( {{s_3},0} \right)} \right]$

Table 2

Table 3

Initial collective risk evaluation matrix $ {\tilde {\boldsymbol{A}}^{{\boldsymbol{c}},{\boldsymbol{0}}}} = {(\tilde {\boldsymbol{a}}_{{\boldsymbol{ij}}}^{{\boldsymbol{c}},{\boldsymbol{0}}})_{{\boldsymbol{m}} {\boldsymbol{\times}} {\boldsymbol{n}}}}\left( {{\boldsymbol{t}} {\boldsymbol{=}} {\boldsymbol{0}}} \right) $"

${F_i}$	${\mathrm{S}}$	${\mathrm{O}}$	$ {\mathrm{D}} $
${F_1}$	$\left[ {\left( {{s_1},0.07} \right),\left( {{s_2},0.03} \right)} \right]$	$\left[ {\left( {{s_1},0.07} \right),\left( {{s_2},0.00} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.04} \right),\left( {{s_3}, - 0.06} \right)} \right]$
${F_2}$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_3},0.00} \right)} \right]$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_2},0.06} \right)} \right]$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_3}, - 0.02} \right)} \right]$
${F_3}$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_3}, - 0.05} \right)} \right]$	$\left[ {\left( {{s_1},0.03} \right),\left( {{s_1},0.06} \right)} \right]$	$\left[ {\left( {{s_3},0.02} \right),\left( {{s_4}, - 0.06} \right)} \right]$
${F_4}$	$\left[ {\left( {{s_3}, - 0.08} \right),\left( {{s_3},0.04} \right)} \right]$	$\left[ {\left( {{s_2},0.05} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_3}, - 0.03} \right),\left( {{s_3},0.02} \right)} \right]$
${F_5}$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_3}, - 0.07} \right)} \right]$	$\left[ {\left( {{s_2},0.05} \right),\left( {{s_3},0.00} \right)} \right]$
${F_6}$	$\left[ {\left( {{s_3},0.04} \right),\left( {{s_3},0.06} \right)} \right]$	$\left[ {\left( {{s_3}, - 0.07} \right),\left( {{s_3},0.08} \right)} \right]$	$\left[ {\left( {{s_3},0.00} \right),\left( {{s_3},0.03} \right)} \right]$
${F_7}$	$\left[ {\left( {{s_3}, - 0.01} \right),\left( {{s_3},0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_2},0.02} \right)} \right]$	$\left[ {\left( {{s_2},0.05} \right),\left( {{s_3}, - 0.02} \right)} \right]$
${F_8}$	$\left[ {\left( {{s_3}, - 0.03} \right),\left( {{s_4}, - 0.06} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.04} \right),\left( {{s_2},0.06} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.03} \right),\left( {{s_2},0.01} \right)} \right]$
${F_9}$	$\left[ {\left( {{s_3}, - 0.02} \right),\left( {{s_3},0.04} \right)} \right]$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_3}, - 0.04} \right)} \right]$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_3},0.07} \right)} \right]$
${F_{10}}$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_3}, - 0.02} \right)} \right]$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_3}, - 0.02} \right)} \right]$
${F_{11}}$	$\left[ {\left( {{s_2},0.08} \right),\left( {{s_3}, - 0.07} \right)} \right]$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_2},0.08} \right)} \right]$
${F_{12}}$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_3}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.03} \right),\left( {{s_2},0.02} \right)} \right]$	$\left[ {\left( {{s_4}, - 0.07} \right),\left( {{s_4},0.05} \right)} \right]$
${F_{13}}$	$\left[ {\left( {{s_2},0.01} \right),\left( {{s_3}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.07} \right),\left( {{s_2},0.05} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_2},0.05} \right)} \right]$
${F_{14}}$	$\left[ {\left( {{s_2}, - 0.02} \right),\left( {{s_3}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.02} \right),\left( {{s_2},0.00} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.07} \right),\left( {{s_2},0.04} \right)} \right]$
${F_{15}}$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_1},0.07} \right),\left( {{s_2}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_2},0.07} \right)} \right]$
${F_{16}}$	$\left[ {\left( {{s_2}, - 0.08} \right),\left( {{s_2},0.03} \right)} \right]$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_3}, - 0.06} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_3}, - 0.07} \right)} \right]$
${F_{17}}$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.05} \right)} \right]$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_2},0.06} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.07} \right),\left( {{s_2},0.01} \right)} \right]$

Table 3

Table 4

Similarity level $ {\bf{SL}}_{{\boldsymbol{ij}}}^{{\boldsymbol{k}},{\boldsymbol{0}}} $ regarding a position in two risk evaluation matrices $ {\tilde {\boldsymbol{A}}^{{\boldsymbol{k}},{\boldsymbol{0}}}} $ and $ {\tilde {\boldsymbol{A}}^{{\boldsymbol{c}},{\boldsymbol{0}}}} $"

${F_i}$	${{\mathrm{TM}}_1}$			${{\mathrm{TM}}_2}$			${{\mathrm{TM}}_3}$			${{\mathrm{TM}}_4}$			${{\mathrm{TM}}_5}$
${F_i}$	${\mathrm{S}}$	${\mathrm{O}}$	${\mathrm{D}}$	${\mathrm{S}}$	${\mathrm{O}}$	$ {\mathrm{D}} $	${\mathrm{S}}$	${\mathrm{O}}$	${\mathrm{D}}$	${\mathrm{S}}$	${\mathrm{O}}$	$ {\mathrm{D}} $	${\mathrm{S}}$	${\mathrm{O}}$	${\mathrm{D}}$
F₁	0.883	0.864	0.787	0.951	0.886	0.797	0.867	0.965	0.880	0.951	0.886	0.880	0.951	0.886	0.880
F₂	0.803	0.712	0.939	0.781	0.788	0.895	0.943	0.871	0.957	0.864	0.788	0.957	0.864	0.788	0.957
F₃	0.907	0.952	0.898	0.926	0.952	0.686	0.843	0.798	0.958	0.957	0.952	0.958	0.957	0.952	0.936
F₄	0.772	0.892	0.842	0.895	0.927	0.908	0.811	0.892	0.825	0.895	0.774	0.908	0.811	0.774	0.975
F₅	0.949	0.965	0.976	0.967	0.951	0.976	0.967	0.965	0.976	0.967	0.951	0.941	0.967	0.951	0.941
F₆	0.713	0.755	0.852	0.871	0.828	0.981	0.621	0.745	0.815	0.787	0.912	0.898	0.879	0.912	0.981
F₇	0.833	0.989	0.964	0.833	0.927	0.934	0.985	0.989	0.934	0.750	0.989	0.964	0.985	0.989	0.900
F₈	0.953	0.927	0.841	0.953	0.823	0.743	0.880	0.761	0.841	0.931	0.823	0.826	0.953	0.823	0.743
F₉	0.657	0.825	0.768	0.760	0.758	0.899	0.593	0.758	0.899	0.843	0.908	0.732	0.974	0.925	0.899
F₁₀	0.816	0.852	0.831	0.767	0.814	0.669	0.816	0.769	0.919	0.767	0.731	0.975	0.684	0.731	0.919
F₁₁	0.909	0.874	0.950	0.927	0.959	0.950	0.924	0.959	0.947	0.924	0.876	0.947	0.924	0.876	0.883
F₁₂	0.859	0.975	0.742	0.960	0.975	0.924	0.891	0.912	0.841	0.891	0.838	0.758	0.891	0.912	0.841
F₁₃	0.950	0.927	0.809	0.951	0.844	0.941	0.716	0.740	0.608	0.867	0.906	0.974	0.867	0.844	0.974
F₁₄	0.867	0.989	0.929	0.883	0.910	0.904	0.883	0.827	0.947	0.883	0.840	0.947	0.950	0.840	0.904
F₁₅	0.899	0.859	0.781	0.935	0.960	0.885	0.935	0.891	0.802	0.935	0.891	0.885	0.935	0.891	0.885
F₁₆	0.859	0.959	0.867	0.725	0.768	0.883	0.942	0.732	0.784	0.891	0.934	0.633	0.891	0.851	0.883
F₁₇	0.754	0.711	0.859	0.829	0.789	0.960	0.663	0.789	0.891	0.958	0.873	0.891	0.913	0.789	0.891

Table 4

Table 5

Consensual collective evaluation matrix"

${F_i}$	${\mathrm{S}}$	${\mathrm{O}}$	$ {\mathrm{D}} $
${F_1}$	$\left[ {\left( {{s_1},0.07} \right),\left( {{s_2},0.03} \right)} \right]$	$\left[ {\left( {{s_1},0.08} \right),\left( {{s_2},0.01} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.03} \right),\left( {{s_3}, - 0.05} \right)} \right]$
${F_2}$	$\left[ {\left( {{s_2},0.05} \right),\left( {{s_3}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_3}, - 0.08} \right),\left( {{s_3}, - 0.02} \right)} \right]$
${F_3}$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.06} \right)} \right]$	$\left[ {\left( {{s_1},0.04} \right),\left( {{s_1},0.08} \right)} \right]$	$\left[ {\left( {{s_3},0.05} \right),\left( {{s_4}, - 0.06} \right)} \right]$
${F_4}$	$\left[ {\left( {{s_2},0.08} \right),\left( {{s_3},0.03} \right)} \right]$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_2},0.08} \right)} \right]$	$\left[ {\left( {{s_3}, - 0.03} \right),\left( {{s_3},0.01} \right)} \right]$
${F_5}$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_3}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_3}, - 0.06} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3},0.00} \right)} \right]$
${F_6}$	$\left[ {\left( {{s_3},0.04} \right),\left( {{s_3},0.05} \right)} \right]$	$\left[ {\left( {{s_3}, - 0.07} \right),\left( {{s_4}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_3},0.00} \right),\left( {{s_3},0.03} \right)} \right]$
${F_7}$	$\left[ {\left( {{s_3},0.00} \right),\left( {{s_3},0.03} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_2},0.02} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.02} \right)} \right]$
${F_8}$	$\left[ {\left( {{s_3}, - 0.04} \right),\left( {{s_4}, - 0.06} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.03} \right),\left( {{s_2},0.08} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.04} \right),\left( {{s_2}, - 0.01} \right)} \right]$
${F_9}$	$\left[ {\left( {{s_3}, - 0.02} \right),\left( {{s_3},0.03} \right)} \right]$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_3}, - 0.04} \right)} \right]$	$\left[ {\left( {{s_2},0.06} \right),\left( {{s_3},0.06} \right)} \right]$
${F_{10}}$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_3}, - 0.03} \right)} \right]$	$\left[ {\left( {{s_2},0.08} \right),\left( {{s_3},0.00} \right)} \right]$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_3}, - 0.04} \right)} \right]$
${F_{11}}$	$\left[ {\left( {{s_2},0.08} \right),\left( {{s_3}, - 0.07} \right)} \right]$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_2},0.08} \right)} \right]$	$\left[ {\left( {{s_2},0.02} \right),\left( {{s_2},0.06} \right)} \right]$
${F_{12}}$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_3}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.03} \right),\left( {{s_2},0.02} \right)} \right]$	$\left[ {\left( {{s_4}, - 0.06} \right),\left( {{s_4},0.05} \right)} \right]$
${F_{13}}$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_3}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.07} \right),\left( {{s_2},0.06} \right)} \right]$	$\left[ {\left( {{s_2},0.00} \right),\left( {{s_2},0.06} \right)} \right]$
${F_{14}}$	$\left[ {\left( {{s_2}, - 0.01} \right),\left( {{s_3}, - 0.08} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.01} \right),\left( {{s_2},0.01} \right)} \right]$	$\left[ {\left( {{s_1},0.08} \right),\left( {{s_2},0.04} \right)} \right]$
${F_{15}}$	$\left[ {\left( {{s_2},0.07} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_1},0.07} \right),\left( {{s_2}, - 0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.03} \right),\left( {{s_2},0.07} \right)} \right]$
${F_{16}}$	$\left[ {\left( {{s_2}, - 0.08} \right),\left( {{s_2},0.01} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.04} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.01} \right),\left( {{s_3}, - 0.08} \right)} \right]$
${F_{17}}$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_3}, - 0.05} \right)} \right]$	$\left[ {\left( {{s_2},0.04} \right),\left( {{s_2},0.07} \right)} \right]$	$\left[ {\left( {{s_2}, - 0.07} \right),\left( {{s_2},0.01} \right)} \right]$

Table 5

Table 6

Fig 2

Table 7

Fig 3

Fig 4

Fig 5

Fig 6

References 56

1	CAI M, XIAO J M, LUO Q, et al Evidential FMEA method for human reliability assessment. Quality Engineering, 2024, 36 (1): 21- 35. doi: 10.1080/08982112.2023.2215315
2	LIU Z M, ZHAO Y J, LIU P D An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system. Engineering Applications of Artificial Intelligence, 2023, 123, 106319. doi: 10.1016/j.engappai.2023.106319
3	SUN J J, LIU Y M, XU J C, et al A probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory. Computers & Industrial Engineering, 2023, 180, 109251.
4	ZHA Q B, WANG S, ZHANG W, et al Failure mode and effect analysis (FMEA) approach based on avoidance of aggregation discrepancy. IEEE Trans. on Engineering Management, 2023, 5 (20): 17- 32.
5	ADALI E A, TUS A ARAS method based on Z-numbers in FMEA. Quality and Reliability Engineering International, 2023, 39 (7): 3059- 3081. doi: 10.1002/qre.3416
6	MOHAMMED A, GHAITHAN A, AL-YAMI F An integrated fuzzy-FMEA risk assessment approach for reinforced concrete structures in oil and gas industry. Journal of Intelligent & Fuzzy Systems, 2023, 44 (1): 1129- 1151.
7	KUSHWAHA D K, PANCHAL D, SACHDEVA A Performance evaluation of bagasse-based cogeneration power generation plant utilizing IFLT, IF-FMEA and IF-TOPSIS approaches. International Journal of Quality & Reliability Management, 2024, 41 (2): 698- 731.
8	ERVURAL B, AYAZ H I A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach. Engineering Failure Analysis, 2023, 152, 107525. doi: 10.1016/j.engfailanal.2023.107525
9	LIU P D, SHEN M J, GENG Y S Risk assessment based on failure mode and effects analysis (FMEA) and WASPAS methods under probabilistic double hierarchy linguistic term sets. Computers & Industrial Engineering, 2023, 186, 109758.
10	WAN X X, CEN L H, YUE W C, et al Failure mode and effect analysis with ORESTE method under large group probabilistic free double hierarchy hesitant linguistic environment. Advanced Engineering Informatics, 2024, 59, 102353. doi: 10.1016/j.aei.2024.102353
11	ZHANG H The multiattribute group decision making method based on aggregation operators with interval-valued 2-tuple linguistic information. Mathematical and Computer Modelling, 2012, 56 (1/2): 27- 35. doi: 10.1016/j.mcm.2012.01.001
12	LIU H C, LI P, YOU J X, et al A novel approach for FMEA: combination of interval 2-tuple linguistic variables and gray relational analysis. Quality and Reliability Engineering International, 2015, 31 (5): 761- 772. doi: 10.1002/qre.1633
13	LIU Z W, MING X G A methodological framework with rough-entropy-ELECTRE TRI to classify failure modes for co-implementation of smart PSS. Advanced Engineering Informatics, 2019, 42, 100968. doi: 10.1016/j.aei.2019.100968
14	ZHANG Z Y, ZHANG H R, ZHOU L X Zero-carbon measure prioritization for sustainable freight transport using interval 2 tuple linguistic decision approaches. Applied Soft Computing, 2023, 132, 109864. doi: 10.1016/j.asoc.2022.109864
15	LIU F, LIAO H C, WU X G, et al Consensus-based failure mode and effect analysis in group decision-making with incomplete weights of risk factors: case study of stereotactic body radiation therapy for lung cancer. Applied Soft Computing, 2023, 142, 110322. doi: 10.1016/j.asoc.2023.110322
16	HUA Z, JING X C, MARTINEZ L An ELICIT information-based ORESTE method for failure mode and effect analysis considering risk correlation with GRA-DEMATEL. Information Fusion, 2023, 93, 396- 411. doi: 10.1016/j.inffus.2023.01.012
17	ZHANG H J, DONG Y C, XIAO J, et al Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts. Reliability Engineering & System Safety, 2021, 208, 107425.
18	ZHA Q B, WANG S, ZHANG W, et al Failure mode and effect analysis (FMEA) approach based on avoidance of aggregation discrepancy. IEEE Trans. on Engineering Management, 2023, 56 (2): 43- 39.
19	MANDAL P, SAMANTA S, PAL M Failure mode and effects analysis in consensus-based GDM for surface-guided deep inspiration breath-hold breast radiotherapy for breast cancer under the framework of linguistic Z-number. Information Sciences, 2024, 658, 120016. doi: 10.1016/j.ins.2023.120016
20	WANG Y, CAI M, JIAN X L Consensus model of social network group decision-making based on trust relationship among experts and expert reliability. Journal of Systems Engineering and Electronics, 2023, 34 (6): 1576- 1588. doi: 10.23919/JSEE.2023.000021
21	DUAN C Y, CHEN X Q, SHI H, et al A new model for failure mode and effects analysis based on k-means clustering within hesitant linguistic environment. IEEE Trans. on Engineering Management, 2022, 69 (5): 1837- 1847. doi: 10.1109/TEM.2019.2937579
22	HAN M S, LEE J S Graph-based density peak merging for identifying multi-peak clusters. Applied Soft Computing, 2023, 146, 110657. doi: 10.1016/j.asoc.2023.110657
23	RODRIGUEZ A L. A clustering by fast search and find of density peaks. Science, 2004, 344(6191): 1492−1496.
24	ZHANG Z X, YANG L, CAO Y N, et al An improved FMEA method based on ANP with probabilistic linguistic term sets. International Journal of Fuzzy Systems, 2022, 24 (6): 2905- 2930. doi: 10.1007/s40815-022-01302-2
25	CHEN C, ZHU W X, MA C H, et al Image motion feature extraction for recognition of aggressive behaviors among group-housed pigs. Computers and Electronics in Agriculture, 2017, 142, 380- 387. doi: 10.1016/j.compag.2017.09.013
26	SUN L, QIN X Y, DING W P, et al Density peaks clustering based on k-nearest neighbors and self-recommendation. International Journal of Machine Learning and Cybernetics, 2021, 12 (7): 1913- 1938. doi: 10.1007/s13042-021-01284-x
27	SONG X, LI S H, QI Z Q, et al A spectral clustering algorithm based on attribute fluctuation and density peaks clustering algorithm. Applied Intelligence, 2023, 53 (9): 10520- 10534. doi: 10.1007/s10489-022-04058-2
28	ZHANG H, DONG Y, PALOMARES-CARRASCOSA I, et al Failure mode and effect analysis in a linguistic context: a consensus-based multiattribute group decision-making approach. IEEE Trans. on Reliability, 2019, 68 (2): 566- 582. doi: 10.1109/TR.2018.2869787
29	HUANG G Q, XIAO L M Failure mode and effect analysis: an interval-valued intuitionistic fuzzy cloud theory-based method. Applied Soft Computing, 2021, 98, 106834. doi: 10.1016/j.asoc.2020.106834
30	HUANG J, XU D H, LIU H C, et al A new model for failure mode and effect analysis integrating linguistic Z-numbers and projection method. IEEE Trans. on Fuzzy Systems, 2021, 29 (3): 530- 538. doi: 10.1109/TFUZZ.2019.2955916
31	BHATTACHARJEE P, DEY V, MANDAL U K Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Safety Science, 2020, 132, 104967. doi: 10.1016/j.ssci.2020.104967
32	YU Y, YANG J, WU S B A novel FMEA approach for submarine pipeline risk analysis based on IVIFRN and ExpTODIM-PROMETHEE-II. Applied Soft Computing, 2023, 136, 110065. doi: 10.1016/j.asoc.2023.110065
33	TANG M, LIAO H C Failure mode and effect analysis considering the fairness-oriented consensus of a large group with core-periphery structure. Reliability Engineering & System Safety, 2021, 215, 107821.
34	FAN C H, ZHU Y, LI W, et al Consensus building in linguistic failure mode and effect analysis: a perspective based on prospect theory. Quality and Reliability Engineering International, 2020, 36 (7): 2521- 2546. doi: 10.1002/qre.2714
35	SHI H, LIU Z, LIU H C A new linguistic preference relation-based approach for failure mode and effect analysis with dynamic consensus reaching process. Information Sciences, 2022, 610 (1): 977- 993.
36	XIAO J, WANG X L, ZHANG H J Exploring the ordinal classifications of failure modes in the reliability management: an optimization-based consensus model with bounded vonfidences. Group Decision and Negotiation, 2022, 21 (1): 49- 80.
37	LIU H C, YOU J X, LI P, et al Failure mode and effect analysis under uncertainty: an integrated multiple criteria decision making approach. IEEE Trans. on Reliability, 2016, 65 (3): 1380- 1392. doi: 10.1109/TR.2016.2570567
38	GHOUSHCHI S J, YOUSEFI S, KHAZAEILI M An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing, 2019, 81, 105505. doi: 10.1016/j.asoc.2019.105505
39	PINTELON L, DI NARDO M, MURINO T, et al A new hybrid MCDM approach for RPN evaluation for a medical device prototype. Quality and Reliability Engineering International, 2021, 37 (5): 2189- 2212. doi: 10.1002/qre.2852
40	NIE W B, LIU W D, WU Z Y, et al 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, 35 (6): 1676- 1697. doi: 10.1002/qre.2468
41	HUANG J, LIU H C, DUAN C Y, et al An improved reliability model for FMEA using probabilistic linguistic term sets and TODIM method. Annals of Operations Research, 2019, 312 (1): 235- 258.
42	SAGNAK M, KAZANCOGLU Y, OZKAN O Y D, et al Decision-making for risk evaluation: integration of prospect theory with failure modes and effects analysis (FMEA). International Journal of Quality & Reliability Management, 2021, 37 (6/7): 939- 956.
43	RATHORE R, THAKKAR J J, JHA J K Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR. International Journal of Quality & Reliability Management, 2020, 38 (2): 551- 580.
44	LI Y, LIU P D, LI G An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information. Reliability Engineering & System Safety, 2023, 235, 109196.
45	LIU H C, CHEN X Q, YOU J X, et al A new integrated approach for risk evaluation and classification with dynamic expert weights. IEEE Trans. on Reliability, 2021, 70 (1): 163- 174. doi: 10.1109/TR.2020.2973403
46	HERRERA F, MARTINEZ L A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. on Fuzzy Systems, 2000, 8 (6): 746- 752. doi: 10.1109/91.890332
47	LIU H C, LIN Q L, WU J Dependent interval 2-tuple linguistic aggregation operators and their application to multiple attribute group decision making. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2014, 22 (5): 717- 735. doi: 10.1142/S0218488514500366
48	ZHANG H, PALOMARES I, DONG Y, et al Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: an approach based on social network analysis. Knowledge-Based Systems, 2018, 162, 29- 45. doi: 10.1016/j.knosys.2018.06.008
49	VICTOR P, CORNELIS C, DE COCK M, et al Practical aggregation operators for gradual trust and distrust. Fuzzy Sets and Systems, 2011, 184 (1): 126- 147. doi: 10.1016/j.fss.2010.10.015
50	TAN X, ZHU J J, PALOMARES I, et al On consensus reaching process based on social network analysis in uncertain linguistic group decision making: exploring limited trust propagation and preference modification attitudes. Information Fusion, 2022, 78 (6): 180- 198.
51	YAGER R Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 1998, 11 (1): 49- 73. doi: 10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z
52	ZHA Q B, DONG Y C, ZHANG H J, et al A personalized feedback mechanism based on bounded confidence learning to support consensus reaching in group decision making. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2021, 51 (6): 3900- 3910. doi: 10.1109/TSMC.2019.2945922
53	ZHU J H, CHEN Z S, SHUAI B, et al Failure mode and effect analysis: a three-way decision approach. Engineering Applications of Artificial Intelligence, 2021, 106 (5): 104505.
54	LO H W, LIOU J J H, HUANG C N, et al A novel failure mode and effect analysis model for machine tool risk analysis. Reliability Engineering & System Safety, 2019, 183, 173- 183.
55	YANG H J, LI G F, HE J L, et al Improved FMEA based on IVF and fuzzy VIKOR method: a case study of workpiece box system of CNC gear milling machine. Quality and Reliability Engineering International, 2021, 37 (6): 2478- 2498. doi: 10.1002/qre.2870
56	WANG L, HU Y P, LIU H C, et al A linguistic risk prioritization approach for failure mode and effects analysis: a case study of medical product development. Quality and Reliability Engineering International, 2019, 35 (6): 1735- 1752. doi: 10.1002/qre.2472

Number	Failure mode	Failure cause	Corrective measure
1	Loose of the lower guide rail	Loose bolts	Fastening bolt
2	Deformation of the lower guide rail	Material defects	Improvement design
3	Position misalignment of the lower guide rail	Inadequate adjustment	Adjust position
4	Position misalignment of the upper guide rail	Inadequate adjustment	Adjust position
5	Loose of the balance pressure roller	Inadequate adjustment	Adjust pressure roller
6	Gap misalignment of the balance pressure roller	Inadequate adjustment	Adjust pressure roller
7	Wear of roller	Long time use	Replacing roller
8	Abnormal roller position	Inadequate adjustment	Adjust position
9	Cracks of carrying portal frame	Material defects	Improvement design
10	Cracks in guide sleeve of carrying portal frame	Material defects	Replacing guide sleeve
11	Loose of carrying portal frame	Inadequate adjustment	Fastening bolt
12	Poor lubrication of long guide pillar	Long-term unlubricated	Add lubricating oil
13	Position misalignment of long guide pillar	Inadequate adjustment	Adjust position
14	Loose of short guide pillar	Loose support	Sturdy guide pillar
15	Poor lubrication of short guide pillar	Lack of lubricating oil	Add lubricating oil
16	Position misalignment of the lower swing arm	Inadequate adjustment	Adjust position
17	Loose of the lower swing arm	Inadequate reinforcement	Fastening lower swing arm

F_i	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1	0.000	0.067	0.067	0.083	0.047	0.129	0.086	0.097	0.098	0.075	0.059	0.088	0.038	0.034	0.042	0.045	0.057
2	0.067	0.000	0.069	0.018	0.025	0.063	0.035	0.052	0.032	0.028	0.018	0.054	0.035	0.047	0.046	0.059	0.035
3	0.067	0.069	0.000	0.073	0.069	0.119	0.070	0.095	0.088	0.093	0.076	0.046	0.062	0.074	0.049	0.095	0.086
4	0.083	0.018	0.073	0.000	0.041	0.049	0.030	0.051	0.017	0.037	0.034	0.049	0.051	0.064	0.058	0.076	0.050
5	0.047	0.025	0.069	0.041	0.000	0.083	0.057	0.071	0.056	0.030	0.024	0.062	0.028	0.035	0.044	0.036	0.033
6	0.129	0.063	0.119	0.049	0.083	0.000	0.066	0.073	0.035	0.061	0.074	0.084	0.097	0.107	0.106	0.110	0.085
7	0.086	0.035	0.070	0.030	0.057	0.066	0.000	0.029	0.033	0.057	0.041	0.061	0.051	0.063	0.049	0.091	0.052
8	0.097	0.052	0.095	0.051	0.071	0.073	0.029	0.000	0.047	0.064	0.048	0.090	0.060	0.067	0.062	0.098	0.051
9	0.098	0.032	0.088	0.017	0.056	0.035	0.033	0.047	0.000	0.042	0.044	0.062	0.064	0.076	0.072	0.087	0.057
10	0.075	0.028	0.093	0.037	0.030	0.061	0.057	0.064	0.042	0.000	0.027	0.076	0.048	0.053	0.066	0.049	0.034
11	0.059	0.018	0.076	0.034	0.024	0.074	0.041	0.048	0.044	0.027	0.000	0.070	0.025	0.033	0.040	0.051	0.017
12	0.088	0.054	0.046	0.049	0.062	0.084	0.061	0.090	0.062	0.076	0.070	0.000	0.072	0.087	0.070	0.095	0.086
13	0.038	0.035	0.062	0.051	0.028	0.097	0.051	0.060	0.064	0.048	0.025	0.072	0.000	0.015	0.020	0.049	0.026
14	0.034	0.047	0.074	0.064	0.035	0.107	0.063	0.067	0.076	0.053	0.033	0.087	0.015	0.000	0.030	0.044	0.026
15	0.042	0.046	0.049	0.058	0.044	0.106	0.049	0.062	0.072	0.066	0.040	0.070	0.020	0.030	0.000	0.067	0.044
16	0.045	0.059	0.095	0.076	0.036	0.110	0.091	0.098	0.087	0.049	0.051	0.095	0.049	0.044	0.067	0.000	0.048
17	0.057	0.035	0.086	0.050	0.033	0.085	0.052	0.051	0.057	0.034	0.017	0.086	0.026	0.026	0.044	0.048	0.000

Cluster	Failure mode
High-risk cluster	${{\mathrm{FM}}_2},{{\mathrm{FM}}_6},{{\mathrm{FM}}_8},{{\mathrm{FM}}_9},{{\mathrm{FM}}_{12}}$
Medium-risk cluster	${{\mathrm{FM}}_4},{{\mathrm{FM}}_5},{{\mathrm{FM}}_7},{{\mathrm{FM}}_{10}},{{\mathrm{FM}}_{11}}$
Low-risk cluster	${{\mathrm{FM}}_1},{{\mathrm{FM}}_3},{{\mathrm{FM}}_{13}},{{\mathrm{FM}}_{14}},{{\mathrm{FM}}_{15}},{{\mathrm{FM}}_{16}},{{\mathrm{FM}}_{17}}$

New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence

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