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Showing 3 results for Human Reliability
Investigating the Effects of Human Personality on Human Reliability and Accident Frequency Using DISC and HEART Technique in Automotive Industries
Elham Pakdel, Manochehr Omidvari,
Volume 5, Issue 1 (6-2017)
Abstract

Introduction: One of the most important issues in industries is accident. Various factors affect these events, one of which is individual features. This study aimed at investigating the role of human resource personality on human reliability in accident outbreaks using DISC behavioral approach modal in automotive industry.

Methods: This was a descriptive-analytical research. The relationship of person vulnerability with human personality with fallibility was investigated. In order to determine human personality disk standard model was used and to estimate the degree of human fallibility heart techniques were used. Then, the relationships of natural personality, human reconcilable dimensions, human reliability and fallibility were found. Also, the relationship of accident repetition with personality and human fallibility was determined. This research was conducted among 98 personnel of one of the biggest production units during 2013 to 2015.

Results: The results indicated that there was a high correlation in human personality and fallibility dimension with accidents outbreak. There was a significant relationship between persons with influential personality, inherent stability, adapted stability, adapted dutiful, and accident outbreak repetition.

Conclusions:  According to the results, the personality of individuals with high sensitivity in their job and less adaptation to changes made in work environment, has a higher possibility of accidents outbreaks. Perhaps, this issue is created because of the inconsistency between the management system that is ruling the industry of developing countries and personality features of those people.


Identification and Evaluation of Fault in Human-Machine Interactive System using CREAM Technique and Fault Tree Analysis
Nooshin Atashfeshan, Prof Mohammad Saidi-Mehrabad, Hamideh Razavi,
Volume 9, Issue 3 (12-2021)
Abstract
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.

An Analytical Perspective on Human Reliability Assessment and Human Error Risk Studies (2010–2023): Exploring the Role of Artificial Intelligence and Lights-Out Manufacturing
Ali Reza Nikray, Dr. Mohammad Reza Vesali Naseh, Abbas Mohammadi,
Volume 13, Issue 3 (9-2025)
Abstract
Objectives: As industrial systems become increasingly complex and technologically advanced, the human role in ensuring safety and efficiency remains indispensable. This study presents a comprehensive review of Human Reliability Assessment researches published between 2010-2023. It compares HRA methodologies with emerging technologies such as artificial intelligence and lights-out manufacturing, identifies existing research gaps, and analyzes both the analytical techniques employed and the industrial sectors addressed.
Methods: A systematic search of major scientific databases was conducted using domain-specific keywords, yielding over 230 publications. Following the removal of duplicates studies, 180 articles were selected for detailed analysis. Each article was evaluated based on methodology, industrial application, country and institutional affiliation, and publishing outlet.
Results: The results indicate that SHERPA, CREAM, and Fuzzy Mathematics are the most frequently applied approaches in HRA research. The United States, China, and South Korea emerged as leading contributors to the field. The findings reveal that neither qualitative nor quantitative methods alone are sufficient to fulfill the three core objectives of HRA: error identification, probability estimation, and control design. A hybrid approach is therefore recommended through the integration of SHERPA and TESEO. SHERPA offers comprehensive coverage of error identification and designing effective control measures, while TESEO facilitates rapid and conservative probability estimation. Together, these methods provide a practical and efficient framework for achieving HRA objectives within operational constraints. Additionally, ten key research gaps were identified.
Conclusion: The SHERPA–TESEO hybrid framework presents a viable strategy for achieving the core goals of HRA. Nonetheless, in the context of smart environments and operator-free production, the shift from static to dynamic and data-driven models is necessary. Recommended developments include revising SHERPA’s cognitive task classifications, recalibrating TESEO’s adjustment factors, and integrating real-time data with human–AI interaction. These advancements are expected to significantly enhance real-time prediction of human-error-risks and support timely intervention strategies.

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