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Abstract：
The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the \"curse of dimensionality\", which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system\'s non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.
摘要：
马尔可夫法是一种常用的可靠性评估方法。它通常用于描述系统的动态特性，比如它的可修复性，故障序列和多个退化状态。然而,“维度的诅咒”，是指随着系统复杂度的增加,系统状态空间呈指数增长,对基于马尔可夫方法的复杂系统可靠性评估提出了挑战。为了应对这一挑战，提出了一种基于非齐次马尔可夫过程的复杂系统可靠性评估方法。这种方法需要将复杂的系统分解为多级子系统，每个都有相对较小的状态空间，根据系统功能。自下而上为每个子系统/系统建立齐次马尔可夫模型或非齐次马尔可夫模型。为了利用下层子系统模型的结果作为上层子系统模型的输入，提出了一种将子系统的不可用性曲线转换为其相应的2×2动态状态转移概率矩阵（STPM）的算法。然后将STPM用作上层系统的非齐次马尔可夫模型的输入。基于所提出的方法，使用反应堆保护系统（RPS）的可靠性评估进行了案例研究，然后将其与基于其他两种对比方法的模型进行比较。通过对比验证了该方法的有效性和准确性。
