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Risk evaluation model for failure mode and effect analysis using intuitionistic fuzzy rough number approach

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Abstract

Failure mode and effect analysis (FMEA) is a multidisciplinary team-based reliability analysis tool used in a wide range of industries. However, the conventional FMEA technique has been criticized for some important deficiencies regarding risk assessments, the weights of experts and risk factors, and risk priority. Although numerous fuzzy-based modified FMEA models have been developed to improve the classical FMEA, the previous approaches lack the ability to handle internal and external uncertainties simultaneously, and they rely on extra assumptions. Hence, this study develops a novel integrated FMEA model, which can manipulate various uncertainties and unknown weights of experts and risk factors and contains more risk factors. In this model, a combination of intuitionistic fuzzy set (IFS) and rough number theory, namely intuitionistic fuzzy rough numbers, is developed to handle internal and external uncertainties of risk assessments, where IFS is used to deal with internal uncertainty, and rough number theory is employed to handle external uncertainty. Then, experts and risk factors’ overall weights involving in the FMEA technique are calculated by considering both subjective and objective aspects. In addition, a two-tier structural model containing seven risk factors is established to capture the risk information of failures more comprehensively. Finally, three practical cases to illustrate the effectiveness of the developed FMEA method are presented along with several comparisons to existing approaches. The results demonstrate that the developed method enhanced the results in terms of precise risk assessment and rational failure rankings. Additionally, its performance is superior or at least significantly compared with the original RPN, fuzzy set, intuitionistic fuzzy set, or rough number-FMEA methods.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (51835001) and the National Major Science and Technology Projects of China (2018ZX04032-001).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China

    Guangquan Huang, Liming Xiao & Genbao Zhang

  2. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Guangquan Huang, Liming Xiao & Genbao Zhang

  3. School of Mechanical and Electrical Engineering, Chongqing University of Arts and Science, Chongqing, 402160, China

    Genbao Zhang

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  1. Guangquan Huang
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Correspondence to Liming Xiao.

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Huang, G., Xiao, L. & Zhang, G. Risk evaluation model for failure mode and effect analysis using intuitionistic fuzzy rough number approach. Soft Comput 25, 4875–4897 (2021). https://doi.org/10.1007/s00500-020-05497-0

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