Canonical Variate Nonlinear Principal Component Analysis for Monitoring Nonlinear Dynamic Processes
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- Shang Liangliang
- School of Electrical Engineering, Nantong University
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- Qiu Aibing
- School of Electrical Engineering, Nantong University
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- Xu Peng
- College of Information Science and Engineering, Northeastern University
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- Yu Feng
- College of Information Science and Engineering, Northeastern University
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<p>Conventional multivariate statistical process monitoring methods constrained by the assumptions of linear and normal distributions for the measurements, such as principal component analysis and canonical variate analysis, demonstrate significantly higher fault alarm rates and lower fault detection rates in nonlinear dynamic industrial process monitoring. Kernel principal component analysis (KPCA) based on the radial basis function, has already been applied in numerous nonlinear industrial processes. However, an infinite-dimensional nonlinear mapping might be inefficient and redundant. To improve the efficiency of traditional methods, this study proposes the implementation of canonical variate nonlinear principal component analysis for monitoring nonlinear dynamic processes. The training data are first preprocessed by performing canonical variate analysis to reduce the effects of the dynamic characteristics of the data. Then, the state vectors are projected into a high dimension feature space by an explicit second-order polynomial mapping. The first k principal components and the remaining residual vectors are obtained in the feature space via conventional principal component analysis for fault detection. The combined statistic Qc is proposed for monitoring the variations in the linear and nonlinear residual spaces; its upper control limits can be estimated by the kernel density function. In comparison to the results of KPCA and nonlinear dynamic principal component analysis, the proposed method yielded significantly higher fault detection rates and relatively lower fault alarm rates in a simulated nonlinear dynamic process and the benchmark Tennessee Eastman process.</p>
収録刊行物
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- JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 55 (1), 29-37, 2022-01-20
公益社団法人 化学工学会
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詳細情報 詳細情報について
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- CRID
- 1390009316352763008
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- NII論文ID
- 130008143104
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- NII書誌ID
- AA00709658
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- ISSN
- 18811299
- 00219592
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- NDL書誌ID
- 032057551
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- 使用不可