Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

作     者：DENG Xiaogang TIAN Xuemin 

作者机构：College of Information and Control Engineering China University of Petroleum Qingdao 266580 China 

基　　金：Supported by the National Natural Science Foundation of China (61273160)  the Natural Science Foundation of Shandong Province of China (ZR2011FM014) and the Fundamental Research Funds for the Central Universities (10CX04046A) 

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

年 卷 期：2013年第21卷第2期

页      码：163-170页

摘      要：Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

主 题 词：nonlinear locality preserving projection kernel trick sparse model fault detection 

学科分类：0810[工学-土木类] 08[工学] 0835[0835] 081001[081001] 0802[工学-机械学] 080201[080201] 

核心收录：

D　O　I：10.1016/S1004-9541(13)60454-1

馆 藏 号：203596386...