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Improving Human Reliability Analysis for Railway Systems Using Fuzzy Logic
IEEE Access ( IF 3.9 ) Pub Date : 2021-09-13 , DOI: 10.1109/access.2021.3112527 Lorenzo Ciani , Giulia Guidi , Gabriele Patrizi , Diego Galar
IEEE Access ( IF 3.9 ) Pub Date : 2021-09-13 , DOI: 10.1109/access.2021.3112527 Lorenzo Ciani , Giulia Guidi , Gabriele Patrizi , Diego Galar
The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert’s subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, $\alpha $
-cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach.
中文翻译:
使用模糊逻辑改进铁路系统的人员可靠性分析
国际铁路联盟每年提供一份安全报告,强调人为因素是每年铁路事故的主要原因之一。因此,人类可靠性的研究是基础,它必须包含在每个铁路相关系统的完整可靠性评估中。然而,目前 RARA(铁路行动可靠性评估)是文献中唯一考虑专门为铁路应用定制的人工任务的方法。RARA 的主要缺点是专家主观性的影响以及在没有详尽的错误和事故数据库的情况下对模型参数进行数值评估的难度。本手稿介绍了一种创新的模糊方法,用于评估铁路应用安全关键系统中的人为因素,以解决上述突出显示的问题。模糊逻辑允许通过更类似于人类认知过程的语言变量来简化模型参数的评估。此外,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果, $\alpha $
-cut 理论和质心去模糊化程序。所提出的方法已应用于在铁路信号系统上进行的人工操作。模拟了四个人工任务和两个场景,以分析所提出算法的性能。最后,将该方法的结果与经典的 RARA 程序进行比较,强调以更简单、更简单和更直观的方法获得的符合结果。
更新日期:2021-09-24
中文翻译:
使用模糊逻辑改进铁路系统的人员可靠性分析
国际铁路联盟每年提供一份安全报告,强调人为因素是每年铁路事故的主要原因之一。因此,人类可靠性的研究是基础,它必须包含在每个铁路相关系统的完整可靠性评估中。然而,目前 RARA(铁路行动可靠性评估)是文献中唯一考虑专门为铁路应用定制的人工任务的方法。RARA 的主要缺点是专家主观性的影响以及在没有详尽的错误和事故数据库的情况下对模型参数进行数值评估的难度。本手稿介绍了一种创新的模糊方法,用于评估铁路应用安全关键系统中的人为因素,以解决上述突出显示的问题。模糊逻辑允许通过更类似于人类认知过程的语言变量来简化模型参数的评估。此外,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果,它比经典的确定性方法更好地处理不确定和不完整的数据,并且最大限度地减少了分析师评估的主观性。所提出算法的输出是模糊区间算法的结果,