基于核状态空间ICA的非线性动态过程故障检测方法

针对工业过程的非线性和动态特性,提出一种基于核状态空间独立元分析的故障检测方法.采用核规范变量分析法将非线性动态过程数据映射到核状态空间,得到去相关的状态数据.对状态数据的各时延协方差矩阵进行加权求和得到状态数据的时序结构矩阵,进而建立ICA统计模型,从状态数据中提取独立元特征数据,并构造监控统计量检测过程故障.在Tennessee Eastman过程上的故障检测结果表明,相比于传统的基于动态核主元分析的故障检测方法,该方法更加灵敏地检测到故障的发生,提高故障检测率.

A Nonlinear Dynamic Process Fault Detection Method Based on Kernel State Space Independent Component Analysis

A fault detection method based on kernel state space independent component analysis (KSSICA) was proposed in this paper considering the nonlinear and dynamic characteristics of industrial processes. Kernel canonical variate analysis (KCVA) was adopted to project the nonlinear and dynamic process data into the kernel state space, and the state data which were uncorrelated were obtained. Based on the state data’s time structure matrix which is the weighted sum of the state data’s different time delayed covariance matrices, an ICA statistical model was constructed to extract the independent component feature data from the state data, and the monitoring statistics were built to detect process faults. The fault detection results on the Tennessee Eastman benchmark process demonstrate that the proposed KSSICA based fault detection method can detect the process faults more agilely and obtain a higher fault detection rate than the conventional fault detection method based on dynamic kernel principal component analysis (DKPCA).