Extended failure mode and effect analysis approach based on hesitant fuzzy linguistic Z-numbers for risk prioritisation of nuclear power equipment failures
| Autor: | Ya-hua Liu, Heng-ming Peng, Jian-qiang Wang, Xiao-kang Wang, Tie-li Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology Computer science business.industry General Engineering 02 engineering and technology Nuclear power Reliability engineering 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Fuzzy linguistic 020201 artificial intelligence & image processing business Failure mode and effects analysis |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 40:10489-10505 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-201154 |
| Popis: | The successful diagnosis of nuclear power equipment failures plays a vital role in guaranteeing the safe operation of nuclear power systems. Failure mode and effect analysis (FMEA) is one of the most commonly used methods for identifying potential failures. However, several shortcomings associated with the conventional FMEA method limit its further application. This paper develops an extended FMEA approach based on hesitant fuzzy linguistic Z-numbers (HFLZNs). Firstly, the concept of HFLZNs is proposed to describe the evaluation information, which inherits the prominent features of the hesitant fuzzy linguistic term set and linguistic Z-numbers (LZNs). Secondly, an HFLZN assessment method is developed to determine the weights of risk factors, and the weights of experts are measured based on hesitation degree. Subsequently, considering the psychological characteristics of decision makers, Tomada de Decisão Iterativa Multicritério and LZNs are integrated to obtain the risk ranking of failure modes. Finally, the practicability of the extended FMEA method is proven by an illustrative example concerning the risk evaluation of a nuclear main pump bearing, and its robustness is verified by indepth analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|