Probability of Erroneous Actions by NPP Operating Personnel: The Role of Duration and Intensity of Workload

Background. To substantiate the reliability assessment of the operating personnel at a nuclear power plant (NPP) under conditions of increasing load, it is necessary to specify the existing general patterns of the functional state dynamics depending on the work and sensory load in relation to the specifics of the work of the NPP operator.

Objective. The aim is to determine the influence of psychophysiological reliability on the probability of errors in NPP operating personnel.

Study Participants. The operational personnel of the nuclear power plant consisting of 40 men.

Methods. Methods for assessing functional state indicators were applied using the hardware and software complex of the Electronic Health Monitoring System. Timesheet analysis, as well as the analysis of the number of general, working and emergency signals during shifts were conducted. Methods of statistical data analysis involved the Statistica statistical package and Excel spreadsheets: descriptive statistics, assessment of the distribution of features, data standardisation; ANOVA and post-hoc analysis (η2p, Tukey HSD).

Results. It was found that the probability of errors made by operational personnel was more closely related to the indicators reflecting the functional state than to the characteristics of the workload. Of these, the probability of mission is more closely related to the indicators of states and the influence of sensory load. The probability of an Erroneous Response is related to both the functional state and a large number of workload factors. The influence of the level of sensory load on the functional state of the operator was also established. A low level of sensory load allows maintaining optimal reaction time and adaptation to work activity for up to 5 working days. An average level of sensory load reduces the duration of the optimal performance period, while a high sensory load causes excessive activation and an increase in mental stress even on the first work day.

Conclusions. Four consecutive uninterrupted work days are the critical duration of uninterrupted work that does not affect the functional state of the operator. From the fifth day, accumulated fatigue significantly affects the reaction speed indicators, and most importantly, the degree of conscious control of the reaction. By the eighth day, compensatory functional reserves are depleted. The direction of future research is the analysis of the influence of methods of psychological and psychophysiological support of the functional reliability of workers, developed in the NPP’s psychophysiological laboratories.

References

Abu-Alqumsan, M., Kapeller, C., Hintermüller, C., Guger, C., Peer, A. (2017). Invariance and variability in interaction error-related potentials and their consequences for classification. Journal of Neural Engineering, 14(6), 066015. https://doi.org/10.1088/1741-2552/aa8416

Anokhin, P.K. (1973). Fundamental issues of the general theory of functional systems. Moscow: Nauka Publ. (In Russ.)

Bobko, N.A., Karpenko, O.V., Cherniuk, V.I. (1998). The combined effect of work stress, fatigue and circadian rhythms on the efficiency of mental activity in operators. Fiziolohichnyi zhurnal, 44(5-6), 35–42.

Bodrov, V.A. (2009). Professional fatigue: Fundamental and applied problems. Moscow: Institute of Psychology Russian Academy of Sciences Press. (In Russ.)

Bodrov, V.A., Orlov, V.Ya. (1998). Psychology and reliability: man in technology control systems. Moscow: Institute of Psychology Russian Academy of Sciences Press. (In Russ.)

Gao, Q., Wang, Y., Song, F., Li, Z., Dong, X. (2013). Mental workload measurement for emergency operating procedures in digital nuclear power plants. Ergonomics, 56(7), 1070–1085. https://doi.org/10.1080/00140139.2013.790483

Giese, S., Carr, D., Chahl, J. (2013). Implications for Unmanned Systems Research of Military UAV Mishap Statistics. In: 2013 IEEE Intelligent Vehicles Symposium (IV), (June, 23–26, 2013). Gold Coast: IEEE Publ. https://doi.org/10.1109/IVS.2013.6629628

Halomoan, J., Ramli, K., Sudiana, D., Gunawan, T.S., Salman, M. (2023). A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning. Information, 14(4), 210. https://doi.org/10.3390/info14040210

Havenith, G. (2004). Handbook of Human Factors and Ergonomics Methods. Boca Raton, London, New York, Washington, CRC Press.

Kaptsov, V.A., Kuzmin, V.A. (2015). The state of the main life-support systems of drivers depending on the conditions and factors of train work. Gigiena i sanitariya = Hygiene and Sanitation, (4), 36–39. (In Russ.)

Kim, J.H., Suh, Y.-A., Yim, M.-S. (2018). An Investigation of Human Error Identification Based on Bio-monitoring System (EEG and ECG Analysis). In: H. Ayaz, L. Mazur, (eds.). Advances in Neuroergonomics and Cognitive Engineering. AHFE 2018. Advances in Intelligent Systems and Computing. (pp. 145–151). Cham: Springer Publ.

Komarov, Yu.Ya. (2014). Improving the level of safety in passenger motor transport using an integrated approach to the professional selection of drivers. Avtotransportnoe predpriyatie = Motor Transport Enterprise, (10), 18–22. (In Russ.)

Kosch, T., Matviienko, A., Müller, F., Bersch, J., Katins, C., Schön, D., Mühlhäuser, M. (2022). NotiBike: Assessing Target Selection Techniques for Cyclist Notifications in Augmented Reality. Proceedings of the ACM on Human-Computer Interaction, 6, 1–24. https://doi.org/10.1145/3546732

Kozlov, V.V. (2024). Pilot activity and reliability in focus of the human factor. Moscow: Academia Zhukovskogo Publ. (In Russ.)

Leonova, A.B., Medvedev, V.I. (1981). Functional states of a person in work activity. Moscow: Moscow Univ. Press. (In Russ.)

Liu, J., Aydin, M., Akyuz, E., Arslan, O., Uflaz, E., Kurt, R.E., Turan, O. (2021). Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS). Journal of Marine Science and Technology, 27, 293–306. https://doi.org/10.1007/s00773-021-00834-w

Lynch, K.M., Banks, V.A., Roberts, A.P., Radcliffe, S., Plant, K.L. (2022). Maritime Autonomous Surface Ships: Can we learn from Unmanned Aerial Vehicle incidents using the Perceptual Cycle Model? Ergonomics, 66(4), 772–790. https://doi.org/10.1080/00140139.2022.2126896

Osipenko, A.V. (2015). Study of the dynamics of performance and fatigue of tracking operators based on the physiological characteristics of the visual analyzer. Eurasian Union of Scientists, 4(13), 151–152. (In Russ.)

Ovcharov, E.V. (2005). Human factor in aviation accidents (methodological materials). Moscow: Avikos Publ. (In Russ.)

Serikov, V.V. (2015). A study of the professional reliability structure of locomotive crew members. Zheleznodorozhnaya medicina i professional’naya bioritmologiya = Railway Medicine and Occupational Biorhythmology, (26), 31−44. (In Russ.)

Shutova, S.V., Muravyova, I.V. (2013). Sensorimotor reactions as a characteristic of the functional state of the central nervous system. Vestnik TGU. Matematika i mekhanika = Tomsk State University Journal of Mathematics and Mechanics, 18(5), 2831–2838. (In Russ.)

Wang, P., Houghton, R., Majumdar, A. (2024). Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review. Sensors, 24(12). https://doi.org/10.3390/s24123723

Weyers, B., Burkolter, D., Kluge, A., Luther, W. (2010). User-Centered Interface Reconfiguration for Error Reduction in Human-Computer Interaction. In: Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services. (pp. 52–55). Gold Coast: IEEE Publ. https://doi.org/10.1109/CENTRIC.2010.11

Wilson, G.F., Russell, C.A. (2003). Operator functional state classification using multiple psychophysiological features in an air traffic control task. The Journal of the Human Factors and Ergonomics Society, 45(3). https://doi.org/10.1518/hfes.45.3.381.27252

Zakharov, A.V., Moroz, M.P., Perelygin, V.V. (1988). Evaluation of operator performance using statistical characteristics of a simple visual-motor reaction. Voenno-medicinskij zhurnal = Military Medical Journal, (1), 53–56. (In Russ.)

Zibarev, E.V., Bukhtiyarov, I.V., Serikov, V.V., Kalinina, S.A., Merkulova, A.G. (2020). Assessment of sensory loads in civil aviation pilots. Medicina truda i promyshlennaya ekologiya = Russian Journal of Occupational Health and Industrial Ecology, (7), 435–442. (In Russ.). https://doi.org/10.31089/1026-9428-2020-60-7-435-442