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Identification and Assessment of Factors Influencing Human Reliability in Maintenance Using Fuzzy Cognitive Maps
Author(s) -
Aju kumar V. N.,
Gandhi M. S.,
Gandhi O. P.
Publication year - 2015
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1569
Subject(s) - human reliability , reliability (semiconductor) , fuzzy cognitive map , reliability engineering , identification (biology) , probabilistic logic , fuzzy logic , constraint (computer aided design) , computer science , risk analysis (engineering) , cognition , variety (cybernetics) , human error , engineering , fuzzy set , psychology , fuzzy number , artificial intelligence , business , mechanical engineering , power (physics) , physics , botany , quantum mechanics , neuroscience , biology
Human element forms an inevitable part of maintenance activity and gets affected by a variety of interacting factors, ranging from environmental, organizational, job factors, and so on to personal characteristics, which bring in inherent variability in its reliability. Assessment of impact of these factors is, therefore, critical for human reliability estimation in maintenance. In every probabilistic risk, safety or maintenance analysis, human reliability does act as an effective aspect to assess implications of various aspects of the human performance. But the main constraint with various human reliability analysis methods is in judging the important human performance influencing factors. Because of high degree of uncertainty and variability that characterizes the plant maintenance environment, it is proposed to use the soft computing technique of fuzzy cognitive maps in exploring the importance of performance shaping factors in maintenance scenario. For this purpose, the maintenance environment is modeled in terms of factors affecting human reliability using cognitive maps. The causal relationships among these factors are explored and simulations performed to quantify its effect on the human reliability. The applicability of the methodology is demonstrated through an example. Copyright © 2013 John Wiley & Sons, Ltd.