This article addresses the distributed multiple fault isolation, modeling, and the closed-loop fault estimation under asynchronous switching for high speed train (HST) with switched dynamics, which is composed of traction, coasting, and braking. First, directed-graph-quantum-learning-based multiple-agent system (MAS) classifiers are introduced to characterize the joints effects of multiple faults. Some sufficient conditions are derived under the condition that the multiple fault topology contains a directed spanning tree and cycle edge, and these conditions guarantee that the multiple fault isolation problem can be solved under randomized learning techniques. Then, single-integrator agents are employed to capture the time-varying topology of multiple fault modeling, in which edge agreement and persistence condition are used to guarantee asymptotic consensus. After that, a novel robust fault estimation design along with the switched Lyapunov function and average dwell time is proposed for the possible power actuator faults subject to asynchronous switching and electromagnetic interferences. In addition, switched estimators are designed such that the closed-loop system is asymptotically stable. A multiple fault isolation and estimation case is investigated to validate the application of this methodology.

中文翻译：

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基于有向图学习的切换动态高速列车多故障诊断


本文讨论了具有切换动态的高速列车 (HST) 异步切换下的分布式多故障隔离、建模和闭环故障估计，该列车由牵引、滑行和制动组成。首先，引入基于有向图量子学习的多智能体系统（MAS）分类器来表征多种故障的联合效应。在多故障拓扑包含有向生成树和环边的情况下，推导了一些充分条件，这些条件保证了多故障隔离问题可以在随机学习技术下得到解决。然后，采用单积分器代理来捕获多故障建模的时变拓扑，其中使用边缘一致性和持久性条件来保证渐近一致性。之后，针对异步开关和电磁干扰下可能出现的动力执行器故障，提出了一种新颖的鲁棒故障估计设计以及开关李亚普诺夫函数和平均停留时间。此外，切换估计器的设计使得闭环系统渐近稳定。研究了多个故障隔离和估计案例来验证该方法的应用。