The reliability of a system can be defined as the capability of ensuring the functional properties of the system within a given variability of work conditions, considering the possible deviations due to unexpected events. In most cases, the reliability of a complex system is strongly related to the reliability of its weakest component. In a human-machine-work environment, the worker appears to be the weakest part, since he is considered the least known and more difficult to model component. Therefore, his capability to withstand the fatigue and psychological stress over time affects the safety of operator’s performance and/or the possibility of his failure. Consistently, in the evaluation of man-machine-work complex system, the Human Error Probability (HEP) represents a key element to assess the reliability of the whole system. Traditional Human Reliability Assessment (HRA) approaches do not provide much attention to the cognitive content of tasks performed by workers, and model HRA by adopting approaches developed for machines, not considering factors affecting workers’ behavior such as environmental, psychological, and physical factors as well as learning vs. forgetting, fatigue vs. recovery, and mental vs. physical fatigue phenomena. The aim of this paper is to develop a mathematical model, based on a multi-attribute utility analysis allowing to estimate dynamic variability of the HEP over time. In the model proposed effects of both learning and fatigue on HEP variability are considered. Learning effect is modeled based on Wright's theory. Fatigue and recovery effect is modeled according to a previous published model relating it with the rest schedule in the work shift. Results of numerical simulations show (i) the effect of task nature and of the learning rate on HEP variability over time, and (ii) the effectiveness of the model in evaluating the optimal rest schedule allowing minimizing HEP average values. They are consistent with values obtained in previous experimental works.
A mathematical model of human error probability for cognitive-oriented tasks / Digiesi, S.; Facchini, F.; Mossa, G.; Mummolo, G.. - In: ...SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS. - ISSN 2283-8996. - ELETTRONICO. - 1:(2019), pp. 423-429. (Intervento presentato al convegno 24th Summer School Francesco Turco, 2019 tenutosi a Brescia, Italy nel September 11-13, 2019).
A mathematical model of human error probability for cognitive-oriented tasks
Digiesi S.Writing – Review & Editing
;Facchini F.
Writing – Original Draft Preparation
;Mossa G.Methodology
;Mummolo G.Supervision
2019-01-01
Abstract
The reliability of a system can be defined as the capability of ensuring the functional properties of the system within a given variability of work conditions, considering the possible deviations due to unexpected events. In most cases, the reliability of a complex system is strongly related to the reliability of its weakest component. In a human-machine-work environment, the worker appears to be the weakest part, since he is considered the least known and more difficult to model component. Therefore, his capability to withstand the fatigue and psychological stress over time affects the safety of operator’s performance and/or the possibility of his failure. Consistently, in the evaluation of man-machine-work complex system, the Human Error Probability (HEP) represents a key element to assess the reliability of the whole system. Traditional Human Reliability Assessment (HRA) approaches do not provide much attention to the cognitive content of tasks performed by workers, and model HRA by adopting approaches developed for machines, not considering factors affecting workers’ behavior such as environmental, psychological, and physical factors as well as learning vs. forgetting, fatigue vs. recovery, and mental vs. physical fatigue phenomena. The aim of this paper is to develop a mathematical model, based on a multi-attribute utility analysis allowing to estimate dynamic variability of the HEP over time. In the model proposed effects of both learning and fatigue on HEP variability are considered. Learning effect is modeled based on Wright's theory. Fatigue and recovery effect is modeled according to a previous published model relating it with the rest schedule in the work shift. Results of numerical simulations show (i) the effect of task nature and of the learning rate on HEP variability over time, and (ii) the effectiveness of the model in evaluating the optimal rest schedule allowing minimizing HEP average values. They are consistent with values obtained in previous experimental works.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
