文献类型：期刊文献

英文题名：GMM and optimal principal components-based Bayesian method for multimode fault diagnosis

作者：Jiang, Qingchao[1];Huang, Biao[1];Yan, Xuefeng[2]

机构：[1]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada;[2]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2016

卷号：84

起止页码：338

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20154101372116);WOS:【SCI-EXPANDED(收录号:WOS:000365335000028)】；

基金：The authors gratefully acknowledge the support from Taishan Visiting Scholar Program of Shandong Province, Alberta Innovates Technology Futures, 973 Project of China (2013CB733600), National Natural Science Foundation of China (21176073) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Gaussian mixture model; Principal component analysis; Bayesian method; Process monitoring; Fault diagnosis

摘要：Principal component analysis (PCA) serves as the most fundamental technique in multivariate statistical process monitoring. However, other than determining contributions to a fault from each variable based on the pre-selected major principal components (PCs), the PCA-based fault diagnosis with an optimal selection of PCs is seldom investigated. This paper presents a novel Gaussian mixture model (GMM) and optimal principal components (OPCs)-based Bayesian method for efficient multimode fault diagnosis. First, the GMM and Bayesian inference is utilized to identify the operating mode, and then local PCA model is established in each mode. Second, given that the various principal components (PCs) may contain distinct fault signatures, the behavior of each PC in local PCA is examined and the OPCs are selected through stochastic optimization algorithm. Based on the OPCs, a Bayesian diagnosis system is then formulated to identify the fault statuses in a probability manner. Performance of GMM-OPC Bayesian diagnosis is examined through a numerical example and the Tennessee Eastman challenge process. The efficiency and feasibility are demonstrated. (C) 2015 Elsevier Ltd. All rights reserved.