Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process  

作　　者：Donglei Zheng Le Zhou Zhihuan Song 

机构地区：[1]State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China [2]Automation and Electrical Engineering,Zhejiang University of Science&Technology,Hangzhou 310023,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第8期1465-1476,共12页自动化学报（英文版）

基　　金：supported by Zhejiang Provincial Natural Science Foundation of China(LY19F030003);Key Research and Development Project of Zhejiang Province(2021C04030);the National Natural Science Foundation of China(62003306);Educational Commission Research Program of Zhejiang Province(Y202044842)。

摘　　要：In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

关 键 词：Fault detection kernel method multi-rate process probability principal component analysis(PPCA) 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]