Modelling and Data Analysis
2020. Vol. 10, no. 1, 7–34
doi:10.17759/mda.2020100101
ISSN: 2219-3758 / 2311-9454 (online)
Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems
Abstract
Estimating the infl uence of human factor on the activity of operators of complex technical systems is an important problem for condition monitoring, personnel training and diagnostics. Presented are both an overview and mutual comparisons of the approaches which are useful to reveal the effect of human factor and have already shown their performances in practical applications. Under consideration are: the structural equation modeling, the Bayesian estimations for probabilistic models represented by Markov random processes, the multivariate statistical techniques including the discriminant and cluster analysis as well as wavelet transforms.
General Information
Keywords: Operators of complex technical systems, human factor, сondition monitoring, factor analysis, structural equation modeling, Markov random processes, wavelet transform, multivariate statistical techniques, principal components analysis, multidimensional scaling, cluster analysis.
Journal rubric: Mathematical Modelling
Article type: scientific article
DOI: https://doi.org/10.17759/mda.2020100101
Acknowledgements. This work was performed as a part of the “SAFEMODE” Project (Grant Agreement No 814961) with the fi nancial support of the Ministry of Science and Higher Education of the Russian Federation (Project UID RFMEFI62819X0014).
For citation: Kuravsky L.S., Yuryev G.A., Zlatomrezhev V.I., Yuryeva N.E., Mikhaylov A.Y. Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2020. Vol. 10, no. 1, pp. 7–34. DOI: 10.17759/mda.2020100101.
A Part of Article
Estimating the influence of human factor on the activity of operators of complex technical systems is an important problem for condition monitoring personnel training and diagnostics. Presented are the approaches which are useful to reveal the effect of human factor and have already shown their performances in practical applications.
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