Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems
Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems
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DOI：
10.1109/tcyb.2019.2945629
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发表时间：
2019-10
影响因子：
11.8
通讯作者：
Jingting Zhang;C. Yuan;P. Stegagno;Haibo He;Cong Wang
中科院分区：
文献类型：
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作者：
Jingting Zhang;C. Yuan;P. Stegagno;Haibo He;Cong Wang
本文研究了具有不确定动态的离散非线性系统的小故障检测问题。故障被认为是“小”的意义上，在故障模式的系统轨迹总是保持接近那些在正常模式，故障的大小可以小于系统的不确定动态。提出了一种新的基于自适应动态学习的sFD框架。具体而言，自适应动态学习方法，使用径向基函数神经网络（RBF NN）的第一次开发，以实现局部准确识别系统的不确定动态，其中所获得的知识可以存储和表示在常数RBF NN。在此基础上，设计了一种新的残差系统，该残差系统引入了一种新的绝对测量小故障引起的系统动态变化的机制。一个自适应阈值，然后开发实时sFD决策。进行严格的分析，推导出可检测性条件和sFD时间的分析上限。仿真研究，包括应用到一个三容水箱基准工程系统，进行了证明所提出的方法的有效性和优势。
This article investigates the problem of small fault detection (sFD) for discrete-time nonlinear systems with uncertain dynamics. The faults are considered to be “small” in the sense that the system trajectories in the faulty mode always remain close to those in the normal mode, and the magnitude of fault can be smaller than that of the system’s uncertain dynamics. A novel adaptive dynamics learning-based sFD framework is proposed. Specifically, an adaptive dynamics learning approach using radial basis function neural networks (RBF NNs) is first developed to achieve locally accurate identification of the system uncertain dynamics, where the obtained knowledge can be stored and represented in terms of constant RBF NNs. Based on this, a novel residual system is designed by incorporating a newmechanism of absolute measurement of system dynamics changes induced by small faults. An adaptive threshold is then developed for real-time sFD decision making. Rigorous analysis is performed to derive the detectability condition and the analytical upper bound for sFD time. Simulation studies, including an application to a three-tank benchmark engineering system, are conducted to demonstrate the effectiveness and advantages of the proposed approach.