期刊
JOURNAL OF PROCESS CONTROL
卷 85, 期 -, 页码 159-172出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2019.11.010
关键词
Principal component analysis; Multi-feature extraction; Nonlinear dynamic process; Process monitoring
资金
- National Natural Science Foundation of China [61703371]
- Social Development Project of Zhejiang Provincial Public Technology Research [LGF19F030004]
- Foundation of Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, P. R. China [APCLI1805]
Principal component analysis (PCA) and its modified methods have been widely applied in industrial process monitoring. In practice, industrial processes are with disparate characteristics, the process monitoring system should consider as many process characteristics as possible, such as dynamic and nonlinear characteristics. In this paper, a multi-feature extraction technique based on PCA is proposed for nonlinear dynamic process monitoring. The proposed method integrates dynamic inner PCA (DiPCA), PCA and kernel PCA (KPCA) methods through a serial structure to extract the dynamic, linear and nonlinear features among the process data. Along with the proposed method, the original data space is decomposed into several orthogonal subspaces, in which abnormal variations of different features can be monitored. For real-time process monitoring, a combined Hotelling's T-2 statistic based on the extracted multi-feature and a squared prediction error (SPE or Q) statistic are established. Case studies on a numerical example and the Tennessee Eastman process are carried out to demonstrate the superior process monitoring performance of the proposed method compared with other relevant methods. (C) 2019 Elsevier Ltd. All rights reserved.
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