Personnel Risk in Assessing the Effectiveness of Ground-based Tests of Complex Technical Systems
DOI:
https://doi.org/10.13052/jgeu0975-1416.1012Keywords:
Testing effectiveness, human reliability, reliability growth model, logistic distributionAbstract
The article provides an original approach to taking into account the risk of
personnel in the process of assessing the effectiveness of testing complex
technical systems. Models of assessment and predictive models of personnel
risks based on the study of their qualification level are considered. The advan-
tages and limitations of the given approach to the analysis and accounting of
personnel risks are shown.
Downloads
References
Lind M. ‘The Mathematical Model of Testing Products’, Questions of
Space-rocket Techniques, pp. 3–14, No. 12, 1970.
Krinetsky E.I, and others. Flight tests of aircraft control systems. p. 193,
Aleksandrovskaya L.N. and others. Theoretical basis of the test and
experimental development of complex technical systems, p. 735. 2003.
Krinetsky E.I. and others. Flight tests of rockets and spacecraft. p. 462,
Krinetsky E.I. and others. Aircraft Testing Fundamentals. p. 312, 1989.
Hollnagel E. 1996 ‘Reliability Analysis and Operator Modelling’,
Reliability Engineering and System Safety, pp.327-337, No52, 1996.
Liao H., Forester J., and others. ‘Assessment of HRA method predictions
against operating crew performance: Part I: Study background, design
and methodology’. Reliability Engineering and System Safety, 191,
Liao H., Forester J., and others. ‘Assessment of HRA method predictions
against operating crew performance: Part II: Overall simulator data,
HRA method predictions and intra-method comparison’. Reliability
Engineering and System Safety, 191, 2019.
Liao H., Forester J., and others. ‘Assessment of HRA method predic-
tions against operating crew performance: Part III: Conclusions and
achievements’. Reliability Engineering and System Safety, 191, 2019.
Zang L., He X., Dai L., and others. ‘The Simulatior experimental study
on the Operator Reliability of Qinhan nuclear power plant’. Reliability
Engineering and System Safety, pp. 252-259, No 92, 2007.
Jirgl M., Havlikova M., and others. ‘Monte Carlo Reliability Analysis
of System with Human Operator’. IFAC-PaperOnLine, pp. 272–277,
–25, 2016.
Avanesov V.S. ‘The problem of psychological tests’, Soviet Education,
pp. 6–23, vol. 22, no. 6, 1980.
Donihue M. Meeting the Standards: An Analysis of Eight Grade
Educational Assesment Test Scores in Maine, 2006.
Haertel E. ‘Tests, Test Scores, and Constructs’. Educational Psycholo-
gist, pp. 1–14, No 53, 2018.
Violato C. Statistics and test score interpretation, 2018.
Thissen D., Wainer H. (Eds.). Test Scoring. Journal of Educational
Measurement, pp. 265–268, 39(3), 2002.
A. V. Kirillin and P. A. Iosifov
Engelhardt L., Goldhammer F. ‘Validating Test Score Interpretations
Using Time Information’. Frontiers in Psychology, 2019.
Kleinberg J., Raghu M. ‘Team Performance with Test Scores’. Proceed-
ings of the Sixteenth ACM Conference on Economics and Computa-
tion – EC ’15, 2015.
Rasch G.E. Probabilistic Models for Some Intelligence and Attainment
Tests, 1993.
Peterson J.K. ‘Logistics Models’. Cognitive Science and Technology,
pp. 257–277, 2016.
Hand D. J., van der Linden W. J., & Hambleton R. K. Handbook of
Modern Item Response Theory. Biometrics, pp. 1680, 54(4), 1998.
Aleksandrovskaya L.N., Kirillin A.V., Shumskaya L.P. ‘Mathematical
models in assessing the effectiveness of continuing education’. Quality.
Innovations. Education, pp. 30–34, No8 (111), 2014.
Aleksandrovskaya L.N., Kirillin A.V., Iosifov P.A., Mitrofanova I.P. ‘A
model for evaluating the effectiveness of retraining of specialists in
multilevel studying’. Quality. Innovations. Education, pp. 3–10, 1(116),
Aleksandrovskaya L.N., Kirillin A.V., Borisova E.V. ‘Statistical anal-
ysis of homogeneity of students’ preparedness’. Quality. Innovations.
Education, pp. 5–1, 6(157), 2018.
Tsoularis A., Wallace J. Analysis of logistic growth models. Mathemat-
ical Biosciences, pp. 21–55, 179(1), 2002.