nooshin atashfeshan

Despite contribution to catastrophic accidents, human errors have been generally ignored in the design of human-machine (HM) systems and the determination of the level of automation (LOA). This paper aims to develop a method to estimate the level of automation in the early stage of the design phase considering both human and machine performance.

A quantitative method is used to evaluate the performance of the whole human-machine system by the human-in-the-loop fault tree analysis while a qualitative and cross-sectional method is used to estimate human errors using the CREAM technique. The data are collected from real cases that happened in the control room of the Ferdowsi power plant.

Full automatic option with an average error of 0.013 had the lowest error rate, i.e. 1/8 of the error rate of the manual design. In addition, the CREAM analysis showed that the control room operators were not satisfied with the availability of procedures and Man-Machine Interface and operational support in general. Thus, on average, the reliability of the manual design is less than the reliability of the automatic setting.

High machine reliability has led to the fact that the fully automatic design would be one of the best design choices for human-machine systems. However, based on the previous studies, high automation may have some human-out-of-the-loop shortcomings. Thus, this study proposed solutions to overcome these disadvantages based on the importance of the control parameters or the essence of human involvement in some decision-making and execution tasks.

This study was designed to investigate the effect of mental fatigue on response-time and error-rate of cyclic tasks using a simulated model in ACT-R cognitive architecture. The effects of fatigue on cyclic tasks with high frequency and long duration have not thus far been systematically studied. In the present study، an unvarying cyclic task was modeled in ACT-R (Adaptive Control of Thought-Rational) environment. For each run، the average response-time per cycle and total error-rate were obtained through dividing the total working time and total number of incorrect responses، respectively by the total number of cycles after eliminating the learning phase. Then، the effect of mental fatigue was studied in the cognitive architecture and a fatigue-based multiplier was incorporated into the model. This multiplier was a concentration index which was declined when response-time and error-rate increased. Next، real data were collected through experiments on volunteers and compared with simulated results using analysis of variance (ANOVA). After validation of the model، the impact of fatigue-based multipliers on the response-time and error-rate were analyzed for different iteration numbers. Using an F test at α=0. 05، no significant disagreement between virtual and real data and the validity of the model was shown. Final results for 3-sec cycle tests revealed up to 0. 5 sec increase in the average response-time for each cycle and 12% in the average error-rate. After 1000 iterations of the sample task، the average response-time for each cycle and the average error-rate are increased by 0. 43 sec and 8%، respectively. This confirms the negative impact of mental fatigue on such indices. Such an effect can be administered and controlled by proper rest breaks.