Many real-world engineering systems such as aerospace systems, intelligent transportation systems and high-performance computing systems are designed to complete missions in multiple phases. These types of systems are known as phased-mission systems. Inspired by an industrial heating system, this research proposes a generalized linear sliding window system with phased missions. The proposed system consists of N nodes with M multi-state elements that are subject to degradation. The linear sliding window system fails if the cumulative performance of any r consecutive nodes is less than the pre-determined demand in any phase. The degradation process of each element is modeled by a continuous-time Markov chain. A novel reliability evaluation algorithm is proposed for the linear sliding window system with phased missions by extending the universal generating function technique. Furthermore, the optimal element allocation strategy is determined using the particle swarm optimization. The effectiveness of the proposed algorithm is confirmed by a set of numerical experiments.

许多现实世界的工程系统，例如航空航天系统，智能运输系统和高性能计算系统，都旨在完成多个阶段的任务。这些类型的系统称为相差系统。受工业加热系统的启发，本研究提出了一种具有阶段任务的广义线性滑窗系统。所提出的系统由N个节点组成，其中N个节点具有M个多状态元素，这些元素会降级。如果任何r的累积性能，线性滑动窗系统就会失效在任何阶段，连续节点都小于预定需求。每个元素的降解过程都通过连续时间马尔可夫链进行建模。通过扩展通用生成函数技术，提出了一种具有阶段任务的线性滑窗系统可靠性评估算法。此外，使用粒子群算法确定最佳元素分配策略。一组数值实验证实了该算法的有效性。