In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach.

中文翻译：

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基于确定性学习的一类闭环系统小故障检测

本文基于确定性学习（DL）理论，提出了一种检测一类非线性闭环系统中的小故障的方法。首先，基于DL的神经控制方法和识别方法被用于提取控制工作量的知识，该知识可补偿故障动态（控制工作量的变化）和故障动态（由于故障引起的系统动态变化）。第二，构造两种类型的残差。一种是测量系统动力学的变化，另一种是测量控制工作量的变化。通过组合这些残差，可以生成增强的残差，其中将故障动态特性和控制工作结合起来以诊断故障。结果表明，重大故障信息得到了控制的补偿，主要故障信息的残差增加了一倍。因此，增强了诊断残差中的故障信息。最后，对诊断方案的故障可检测性条件进行了分析。包括仿真研究以证明该方法的有效性。