文献类型：期刊文献

英文题名：A Novel Dynamic Weight Principal Component Analysis Method and Hierarchical Monitoring Strategy for Process Fault Detection and Diagnosis

作者：Tao, Yang[1];Shi, Hongbo[1];Song, Bing[1];Tan, Shuai[1]

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

年份：2020

卷号：67

期号：9

起止页码：7994

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20202208741554);WOS:【SCI-EXPANDED(收录号:WOS:000536291000082)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703161 and Grant 61673173, in part by the Fundamental Research Funds for the Central Universities under Grant 222201714031, and in part by China Postdoctoral Science Foundation under Grant 2017M611472.

语种：英文

外文关键词：Monitoring; Principal component analysis; Heuristic algorithms; Fault detection; Fault diagnosis; Data models; Industries; Dynamic weight principal component analysis (DWPCA); fault detection; fault diagnosis; hierarchical process monitoring

摘要：Traditional monitoring algorithms use the normal data for modeling, which are universal for different types of faults. However, these algorithms may perform poorly sometimes because of the lack of fault information. In order to further increase the fault detection rate while preserving the universality of the algorithm, a novel dynamic weight principal component analysis (DWPCA) algorithm and a hierarchical monitoring strategy are proposed. In the first layer, the dynamic PCA is used for fault detection and diagnosis, if no fault is detected, the following DWPCA-based second layer monitoring will be triggered. In the second layer, the principal components (PCs) are weighted according to its ability in distinguishing between the normal and fault conditions, then the PCs which own larger weight are selected to construct the monitoring model. Compared to the DPCA method, the proposed DWPCA algorithm establishes the monitoring model by combining the information of fault. Afterward, the DWPCA-based variable relative contribution and a novel control limit for the variable relative contribution are presented for the fault diagnosis. Finally, the superiority of the proposed method is demonstrated by a numerical case and the Tennessee Eastman process.