Reduced-Order Distributed Fault Diagnosis for Large-Scale Nonlinear Stochastic Systems
Reduced-Order Distributed Fault Diagnosis for Large-Scale Nonlinear Stochastic Systems
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DOI：
10.1115/1.4037839
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发表时间：
2018-05
影响因子：
1.7
通讯作者：
E. Noursadeghi;I. Raptis
中科院分区：
文献类型：
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作者：
E. Noursadeghi;I. Raptis
本文研究不确定非线性大系统的分布式故障检测与隔离问题。所提出的方法的目标应用程序的计算要求的全阶故障敏感滤波器将是令人望而却步的要求。原来的过程被细分为低阶相互关联的子系统，可能，重叠的状态。诊断单元的网络被部署为以分布式方式监测低阶子系统。每个诊断单元都可以访问其分配的子系统状态的本地和噪声测量，以及来自其相邻节点的经处理的统计信息。诊断算法输出系统状态的过滤估计和每个故障模式的统计置信度的测量。分布式故障敏感滤波器的布局在监控网络的计算和通信要求方面实现了显著的整体复杂性降低和设计灵活性。仿真结果证明了该方法的有效性。
This paper deals with the distributed fault detection and isolation problem of uncertain, nonlinear large-scale systems. The proposed method targets applications where the computation requirements of a full-order failure-sensitive filter would be prohibitively demanding. The original process is subdivided into low-order interconnected subsystems with, possibly, overlapping states. A network of diagnostic units is deployed to monitor, in a distributed manner, the low-order subsystems. Each diagnostic unit has access to a local and noisy measurement of its assigned subsystem's state, and to processed statistical information from its neighboring nodes. The diagnostic algorithm outputs a filtered estimate of the system's state and a measure of statistical confidence for every fault mode. The layout of the distributed failure-sensitive filter achieves significant overall complexity reduction and design flexibility in both the computational and communication requirements of the monitoring network. Simulation results demonstrate the efficiency of the proposed approach.