基于变量子域PCA的故障检测方法

针对工业过程监控中传统主元分析（PCA）方法没有突出局部变量信息的问题，提出一种基于变量子域PCA（variable sub-region PCA，VSR-PCA）的故障检测方法。首先使用PCA将原始数据空间分解成主元子空间（principal component subspace，PCS）和残差子空间（residual subspace，RS），计算变量与PCS的互信息来度量两者的相关性并以此划分变量子域。然后在变量子域中计算局部T²统计量和局部SPE统计量，并通过贝叶斯推理整合所有子域的信息构造全局统计量，使得在利用所有过程信息的同时挖掘局部变量信息。在连续搅拌反应釜系统上的仿真结果表明，VSR-PCA方法具有更好的过程监控性能。

Fault detection method based on variable sub-region PCA

Aiming at the problem that traditional principal component analysis (PCA) method can't highlight the local variable information in industrial process monitoring, this paper proposes a variable sub-region PCA (VSR-PCA) fault detection method. First, PCA is used to decompose original data space into principal component subspace (PCS) and residual subspace (RS), and mutual information between variables and PCS is calculated to measure their correlation which is utilized to obtain the variable sub-regions. Then, local T² statistics and local SPE statistics are calculated in each variable sub-region. Bayesian inference is applied to integrate information in every sub-region to construct global statistics which are able to emphasize the local variable information while preserving the whole process information. Simulation results on the continuous stirred tank reactor (CSTR) system show that VSR-PCA method has better process monitoring performance.