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Volume 9, Issue 3 (Iranian Journal of Ergonomics 2021)
Iran J Ergon 2021, 9(3): 84-103 |
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Atashfeshan N, Saidi-Mehrabad M, Razavi H. Identification and Evaluation of Fault in Human-Machine Interactive System using CREAM Technique and Fault Tree Analysis. Iran J Ergon 2021; 9 (3) :84-103
URL: http://journal.iehfs.ir/article-1-822-en.html
URL: http://journal.iehfs.ir/article-1-822-en.html
1- PhD Candidate, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
2- Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran. ,mehrabad@iust.ac.ir
3- Associate Professor, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2- Professor, Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran. ,
3- Associate Professor, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (5319 Views)
Background and Objectives: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Keywords: Human-Machine Systems, Function Allocation, Human Reliability, Human-in-the-loop fault tree analysis, Human factors
Type of Study: Research |
Subject:
Other Cases
Received: 2021/06/4 | Accepted: 2022/01/30 | ePublished: 2022/01/30
Received: 2021/06/4 | Accepted: 2022/01/30 | ePublished: 2022/01/30
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