期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 67, 期 9, 页码 7994-8004出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2942560
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
资金
- National Natural Science Foundation of China [61703161, 61673173]
- Fundamental Research Funds for the Central Universities [222201714031]
- China Postdoctoral Science Foundation [2017M611472]
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.
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