Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach
Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach
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DOI：
10.1109/access.2020.2991138
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Jingting Zhang;Qingbin Gao;C. Yuan;Weizhen Zeng;Shi‐Lu Dai;Cong Wang
中科院分区：
文献类型：
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作者：
Jingting Zhang;Qingbin Gao;C. Yuan;Weizhen Zeng;Shi‐Lu Dai;Cong Wang
本文将“相似故障”的概念引入离散非线性不确定系统的故障隔离领域，定义了一类新的重要故障，它们在故障幅度和故障引起的系统轨迹上具有较小的相互差异。这种类似故障的有效隔离是相当具有挑战性的，因为它们的小的相互差异可以容易地被其他系统不确定性（例如，建模不确定性/干扰）。为此，提出了一种新的相似故障隔离（sFI）计划的基础上，自适应阈值机制。具体而言，自适应动态学习方法的确定性学习理论的基础上，首先介绍了本地准确地学习/识别不确定系统动态下的每一个故障模式，使用径向基函数神经网络（RBF神经网络）。在此基础上，一个银行的sFI估计，然后开发使用一种新的机制，绝对测量故障动态差异。由此产生的残差信号可以用来有效地捕捉相似故障的微小互差，并将它们与其他系统不确定性区分开来。最后，一个自适应阈值的设计实时sFI决策。所提出的sFI方案的一个重要特征是：它不仅能够隔离属于预定义故障集（用于训练/学习过程）的类似故障，而且能够识别不匹配任何预定义故障的新故障。严格的隔离条件和隔离时间进行分析，以表征所提出的sFI计划的性能。单连杆柔性关节机器人手臂的实际应用实例的仿真结果被用来显示所提出的计划的有效性和优势，现有的方法。
In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches.