In current studies of safety assessment for complex systems with the evidential reasoning (ER) rule, the evidence reliability is generally given by experts, which makes the observation data by sensors ignored. However, sensors are inevitably affected by such various uncertainties as perturbations in engineering practice, which can reduce their quality and tracking ability. As such, the observation data may become unreliable, and the modeling accuracy of the ER rule is decreased. In this article, a new ER rule-based safety assessment method with sensor reliability for complex systems is proposed, where sensor reliability and perturbation are considered. The coefficient of the variation-based weighting (CVBW) method is employed to obtain sensor weight. The sensor reliability is calculated by static reliability and dynamic reliability, which are determined by experts and the distance-based method, respectively. The perturbation is quantified as a bounded parameter defined as the perturbation factor, which is used to describe uncertainties and aggregate static reliability and dynamic reliability. The performance analysis of safety assessment is conducted to demonstrate the rationality of perturbation and position poor sensors, followed by a safety assessment algorithm. A case study is carried out to validate the effectiveness of the proposed method.

中文翻译：

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一种新的基于证据推理规则的复杂系统传感器可靠性安全评估方法

在现有的基于证据推理（ER）规则的复杂系统安全评估研究中，证据可靠性一般由专家给出，忽略了传感器的观测数据。然而，传感器在工程实践中不可避免地受到扰动等各种不确定性的影响，从而降低其质量和跟踪能力。因此，观测数据可能变得不可靠，并且降低了ER规则的建模精度。在本文中，提出了一种新的基于 ER 规则的复杂系统传感器可靠性安全评估方法，其中考虑了传感器可靠性和扰动。采用基于变化的加权（CVBW）方法的系数来获得传感器权重。传感器可靠性按静态可靠性和动态可靠性计算，分别由专家和基于距离的方法确定。扰动被量化为定义为扰动因子的有界参数，用于描述不确定性和聚合静态可靠性和动态可靠性。进行安全评估的性能分析，证明微扰和位置差传感器的合理性，然后提出安全评估算法。进行案例研究以验证所提出方法的有效性。进行安全评估的性能分析，证明微扰和位置差传感器的合理性，然后提出安全评估算法。进行案例研究以验证所提出方法的有效性。进行安全评估的性能分析，证明微扰和位置差传感器的合理性，然后提出安全评估算法。进行案例研究以验证所提出方法的有效性。