Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms
Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms
复制标题
DOI：
10.1109/tie.2017.2733501
复制
发表时间：
2018-02-01
影响因子：
7.7
通讯作者：
Gui, Weihua
中科院分区：
文献类型：
--
作者：
Chen, Zhiwen;Ding, Steven X.;Gui, Weihua
在本文中，我们首先研究了广义典型相关分析（CCA）的故障检测（FD）的方法，旨在最大限度地提高故障检测能力在一个可接受的误报率。更具体地说，产生两个残差信号，分别用于检测输入和输出子空间中的故障。通过考虑输入和输出之间的相关性，实现了两个残差信号的最小协方差。针对广义CCA对过程噪声的高斯假设限制了其应用范围的问题，提出了一种基于随机化算法的门限设置与广义CCA相结合的故障检测技术，并将其应用于高速列车牵引传动控制仿真系统。实验结果表明，与标准的基于CCA的FD方法相比，该方法能够显著提高检测性能。
In this paper, we first study a generalized canonical correlation analysis (CCA)-based fault detection (FD) method aiming at maximizing the fault detectability under an acceptable false alarm rate. More specifically, two residual signals are generated for detecting of faults in input and output subspaces, respectively. The minimum covariances of the two residual signals are achieved by taking the correlation between input and output into account. Considering the limited application scope of the generalized CCA due to the Gaussian assumption on the process noises, an FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains. The achieved results show that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized CCA-based FD method.