Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems

225

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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Information About the Authors

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

Valentin I. Zlatomrezhev, Head of Laboratory, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia, ORCID: https://orcid.org/0000-0003-1776-6881, e-mail: vizlatomr@2100.gosniias.ru

Nataliya E. Yuryeva, PhD in Engineering, Head of Laboratory, Youth Laboratory Information Technologies for Psychological Diagnostics, Research Fellow, Information Technology Center for Psychological Studies of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0003-1419-876X, e-mail: yurieva.ne@gmail.com

Artem Y. Mikhaylov, Engineer, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia, ORCID: https://orcid.org/0000-0003-0278-1819, e-mail: aymihaylov@2100.gosniias.ru

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