期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 65, 期 2, 页码 1508-1517出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2733443
关键词
Linear dynamic system (LDS); nonlinear dynamic processes; probabilistic principal component analysis (PPCA); quality prediction; soft sensor; weighted linear dynamic system (WLDS)
资金
- National Natural Science Foundation of China [61590921, 61573308, 61703440]
- Innovation-driven Plan in Central South University
Industrial process plants are instrumented with a large number of redundant sensors and the measured variables are often contaminated by random noises. Thus, it is significant to discover the general trends of data by latent variable models in the probabilistic framework before soft sensor modeling. However, traditional probabilistic latent variable models such as probabilistic principal component analysis are mostly static linear approaches. The process dynamics and nonlinearities have not been well considered. In this paper, a novel weighted linear dynamic system (WLDS) is proposed for nonlinear dynamic feature extraction. In WLDS, two kinds of weights are proposed for local linearization of the nonlinear state evolution and state emission relationships. In this way, a weighted log-likelihood function is designed and expectation-maximization algorithm is then used for parameter estimation. The feasibility and effectiveness of the proposed method is demonstrated with a numerical example and an industrial process application.
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