Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes

作     者：Yuan Xu Ying Liu Qunxiong Zhu 

作者机构：College of Information Science and TechnologyBeijing University of Chemical Technology 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2016年第24卷第10期

页      面：1413-1422页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：Supported by the National Natural Science Foundation of China(61573051,61472021) the Natural Science Foundation of Beijing(4142039) Open Fund of the State Key Laboratory of Software Development Environment(SKLSDE-2015KF-01) Fundamental Research Funds for the Central Universities(PT1613-05) 

主　　题：Fault prognosis Time delay estimation Local kernel principal component analysis 

摘      要：Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in- cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis. In this approach, mutual information estimation and Bayesian information criterion （BIC） are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation （BP） network is applied whose input is multivar- iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis （LKPCA） model for incipient fault prognosis. The new method has been exemplified in a sim- ple nonlinear process and the complicated Tennessee Eastman （TE） benchmark process. The results indicate that the new method has suoerioritv in the fault prognosis sensitivity over other traditional fault prognosis methods.