IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5694-5705. doi: 10.1109/TNNLS.2021.3071292. Epub 2022 Oct 5.
With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.
本文借助神经网络，为动态系统开发了两种基于数据驱动的故障检测 (FD) 设计。第一个神经网络用于生成所谓的有限脉冲响应 (FIR) 滤波器形式的残差信号，第二个神经网络用于递归生成残差信号。通过理论分析，我们表明，两个提出的神经网络通过自组织学习可以分别找到其最优结构，对应于 FIR 滤波器和递归观测器，用于 FD 目的。本研究的其他贡献在于，我们建立了连接基于模型和基于神经网络的方法的桥梁，用于检测动态系统中的故障。采用三容水箱系统进行实验，验证了两种提出的基于神经网络的 FD 算法的有效性。
