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Intuitionistic fuzzy approach to reliability assessment of multi-state systems. (English) Zbl 1540.90088

Summary: One of the major problems that one encounters within an engineering system is the assessment of its quality, or in other words, reliability. However, conventional reliability tends to ignore the fuzziness in the system, i.e., the lack of precision, vagueness, inaccuracy, or inadequacy in the information obtained. As a result, the integration of fuzzy sets into reliability theory has proven to be incredibly vital. Similarly, the universal generating function (UGF) technique has also been an asset in reliability assessment ever since its inception due to its simplicity. Here, a novel technique involving the intuitionistic fuzzy sets (IFS), specifically using the triangular intuitionistic fuzzy numbers (TIFN) and the UGF method, has been created for the reliability assessment of a fuzzy multi-state system (FMSS). This method is then applied to a system of wireless communication and its intuitionistic fuzzy reliability and availability are evaluated where each performance state and its probability are characterized by a TIFN. The obtained results are also represented graphically for better insights. This work is helpful in getting a clear picture of FMSS and thus, making them more reliable.

MSC:

90B25 Reliability, availability, maintenance, inspection in operations research
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
62N05 Reliability and life testing
Full Text: DOI

References:

[1] Atanassov, K. T., Intuitionistic fuzzy sets, (VII ITKR’s Session, Sofia deposed in Central Sci., Vol. 1697 (1983), Technical Library of Bulg. Acad. of Sci), 84
[2] Bing, L.; Meilin, Z.; Kai, X., A practical engineering method for fuzzy reliability analysis of mechanical structures, Reliab. Eng. Syst. Saf., 67, 3, 311-315 (2000)
[3] Cai, K. Y.; Wen, C. Y.; Zhang, M. L., Fuzzy states as a basis for a theory of fuzzy reliability, Microelectron. Reliab., 33, 15, 2253-2263 (1993)
[4] Chachra, A.; Kumar, A.; Ram, M., Application of fuzzy universal generating function in reliability assessment of thermal power plants, (2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions). 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO (2022), IEEE: IEEE Noida, India), 1-4
[5] Chang, D. Y., Applications of the extent analysis method on fuzzy AHP, European J. Oper. Res., 95, 3, 649-655 (1996) · Zbl 0926.91008
[6] Chaube, S.; Singh, S., Fuzzy reliability of two-stage weighted-k-out-of-n systems with common components, Int. J. Math. Eng. Manag. Sci., 1, 1, 41-51 (2016)
[7] Ding, Y.; Lisnianski, A., Fuzzy universal generating functions for multi-state system reliability assessment, Fuzzy Sets and Systems, 159, 3, 307-324 (2008) · Zbl 1167.90468
[8] Ding, Y.; Wang, P.; Goel, L.; Loh, P. C.; Wu, Q., Long-term reserve expansion of power systems with high wind power penetration using universal generating function methods, IEEE Trans. Power Syst., 26, 2, 766-774 (2010)
[9] Gao, P.; Xie, L.; Hu, W.; Liu, C.; Feng, J., Dynamic fuzzy reliability analysis of multistate systems based on universal generating function, Math. Probl. Eng., 2018, 1-8 (2018)
[10] Hirsch, W. M.; Meisner, M.; Boll, C., Cannibalization in multicomponent systems and the theory of reliability, Nav. Res. Logist. Q., 15, 3, 331-360 (1968) · Zbl 0187.18104
[11] Huang, T.; Xiahou, T.; Li, Y. F.; Qian, H. M.; Liu, Y.; Huang, H. Z., Reliability assessment of wind turbine generators by fuzzy universal generating function, Eksploatacja Niezawodność, 23, 2, 308-314 (2021)
[12] Jiang, Q.; Chen, C. H., A numerical algorithm of fuzzy reliability, Reliab. Eng. Syst. Saf., 80, 3, 299-307 (2003)
[13] Jianqiang, W.; Zhong, Z., Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems, J. Syst. Eng. Electron., 20, 2, 321-326 (2009)
[14] Kabir, S.; Geok, T. K.; Kumar, M.; Yazdi, M.; Hossain, F., A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation, IEEE Access, 8, 980-996 (2019)
[15] Kumar, A.; Ram, M., System reliability analysis based on Weibull distribution and hesitant fuzzy set, Int. J. Math. Eng. Manag. Sci., 3, 4, 513-521 (2018)
[16] Kumar, A.; Singh, S. B.; Ram, M., Reliability appraisal for consecutive-k-out-of-n: F system of non-identical components with intuitionistic fuzzy set, Int. J. Oper. Res., 36, 3, 362-374 (2019)
[17] Kumar, M.; Yadav, S. P., A novel approach for analyzing fuzzy system reliability using different types of intuitionistic fuzzy failure rates of components, ISA Trans., 51, 2, 288-297 (2012)
[18] Kumar, M.; Yadav, S. Prasad.; Kumar, S., Fuzzy system reliability evaluation using time-dependent intuitionistic fuzzy set, Internat. J. Systems Sci., 44, 1, 50-66 (2013) · Zbl 1307.93225
[19] Lata, S.; Kumar, A., A new method to solve time-dependent intuitionistic fuzzy differential equations and its application to analyze the intuitionistic fuzzy reliability of industrial systems, Concurr. Eng., 20, 3, 177-184 (2012)
[20] Levitin, G.; Lisnianski, A., A new approach to solving problems of multi-state system reliability optimization, Qual. Reliab. Eng. Int., 17, 2, 93-104 (2001)
[21] Li, Y. F.; Huang, H. Z.; Mi, J.; Peng, W.; Han, X., Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability, Ann. Oper. Res., 311, 1, 195-209 (2022) · Zbl 1492.60251
[22] Li, G.; Lu, Z.; Xu, J., A fuzzy reliability approach for structures based on the probability perspective, Struct. Saf., 54, 10-18 (2015)
[23] Liang, C.; Zhao, S.; Zhang, J., Aggregation operators on triangular intuitionistic fuzzy numbers and its application to multi-criteria decision making problems, Found. Comput. Decision Sci., 39, 3, 189-208 (2014) · Zbl 1334.91033
[24] Liu, Y.; Huang, H. Z., Reliability assessment for fuzzy multi-state systems, Internat. J. Systems Sci., 41, 4, 365-379 (2010) · Zbl 1301.93147
[25] Liu, Y.; Huang, H. Z.; Levitin, G., Reliability and performance assessment for fuzzy multi-state elements, Proc. Inst. Mech. Eng. O, 222, 4, 675-686 (2008)
[26] Mahapatra, G. S.; Roy, T. K., Reliability evaluation using triangular intuitionistic fuzzy numbers arithmetic operations, World Acad. Sci. Eng. Technol., 50, 574-581 (2009)
[27] Roohanizadeh, Z.; Baloui Jamkhaneh, E.; Deiri, E., The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution, Soft Comput., 1-19 (2022)
[28] Shu, M. H.; Cheng, C. H.; Chang, J. R., Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly, Microelectron. Reliab., 46, 12, 2139-2148 (2006)
[29] V. Torra, Y. Narukawa, On hesitant fuzzy sets and decision, in: 2009 IEEE International Conference on Fuzzy Systems, 2009, pp. 1378-1382, Jeju, Korea (South).
[30] Traneva, V.; Tranev, S., Intuitionistic fuzzy index-matrix selection for the outsourcing providers at a refinery, (International Conference on Intelligent and Fuzzy Systems (2022), Springer: Springer Cham), 119-128
[31] Ushakov, I. A., A universal generating function, Sov. J. Comput. Syst. Sci., 24, 5, 118-129 (1986) · Zbl 0713.05003
[32] R.R. Yager, Pythagorean fuzzy subsets, in: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS, 2013, pp. 57-61, Edmonton, AB, Canada.
[33] Yang, J.; Xing, L.; Wang, Y.; He, L., Combinatorial reliability evaluation of multi-state system with epistemic uncertainty, Int. J. Math. Eng. Manag. Sci., 7, 3, 312-324 (2022)
[34] Zadeh, L. A., Fuzzy sets, Inf. Control, 8, 3, 338-353 (1965) · Zbl 0139.24606
[35] Zhu, B.; Xu, Z.; Xia, M., Dual hesitant fuzzy sets, J. Appl. Math., 2012, 1-13 (2012) · Zbl 1244.03152
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