文献类型：期刊文献

英文题名：Process monitoring based on entropy weight for a subspace containing probabilistic principal components and fault-relevant noise factors

作者：Zhu, Tianxian[1];Huang, Jian[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, POB 293,MeiLong Rd 130, Shanghai 200237, Peoples R China

年份：2017

卷号：31

期号：6

外文期刊名：JOURNAL OF CHEMOMETRICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000403910900003)】；

基金：973 project of China, Grant/Award Number: 2013CB733605; National Natural Science Foundation of China, Grant/Award Number: 21176073; Fundamental Research Funds for the Central Universities

语种：英文

外文关键词：entropy method; fault detection; moving window; weighted probabilistic principal component analysis

摘要：Probabilistic principal component analysis (PPCA) is widely used in process monitoring and has achieved effective fault detection and online fault identification. However, the relationship between fault information and certain probabilistic principal components has not been explicitly mapped. Therefore, fault information might be scattered across 2 different subspaces when probabilistic principal components are selected subjectively based on variance. Moreover, although the maximum useful information is collected into a new subspace by the selected elements, a large amount of useless information might cover up the fault-relevant information. Considering these shortcomings, weighted PPCA based on entropy method and moving window (EMW-PPCA) is applied in this study to improve the monitoring performance of traditional PPCA. After building a traditional PPCA model through normal observations, EMW-PPCA proposes a novel strategy to identify and extract as much fault-relevant information as possible from the residual subspace and then integrates the selected fault-relevant noise factors into the dominant subspace to create a new specific subspace for process monitoring. The entropy method is applied to this new subspace to define the importance degree of each direction and evaluate the weighting values objectively. Then, the weighting values are assigned to each of the corresponding elements to highlight the fault-relevant information for online monitoring within a moving window. Case studies on a simple numerical example are conducted, and the Tennessee Eastman process is applied to validate the effectiveness of the EMW-PPCA scheme.