Evaluating the Contribution of Human Factor to Performance Characteristics of Complex Technical Systems 150
Doctor of Engineering, Dean of the Computer Science Faculty , Moscow State University of Psychology and Education , Moscow, Russia
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology and Education, Moscow, Russia
Head of Laboratory, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
PhD in Engineering, Research Fellow, Information Technology Center for Psychological-Ecological Studies of the Faculty Newsletter-Technologies, Research Associate, Moscow State University of Psychology and Education, Moscow, Russia
Engineer, State Research Institute of Aviation Systems (GosNIIAS), Moscow, Russia
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.
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.
Column: Teaching Methodology
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.
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).
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