| Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis |
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| 作者姓名： | 张曦 阎威武 赵旭 邵惠鹤 |
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| 作者单位： | Dept.of Automation Shanghai Jiaotong Univ.，Dept.of Automation，Shanghai Jiaotong Univ.，Dept.of Automation，Shanghai Jiaotong Univ.，Dept.of Automation，Shanghai Jiaotong Univ.，Shanghai 200240，China，Shanghai 200240，China，Shanghai 200240，China，Shanghai 200240，China |
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| 摘 要： | A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.
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| 关 键 词： | 核心独立成分分析 非线性统计 管理图表 监视系统 |
| 文章编号： | 1007-1172（2007）05-0000-00 |
| 修稿时间： | 2006-07-14 |
Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis |
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| ZHANG Xi,YAN Wei-wu,ZHAO Xu,SHAO Hui-he.Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis[J].Journal of Shanghai Jiaotong university,2007,12(5):563-571. |
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| Authors: | ZHANG Xi YAN Wei-wu ZHAO Xu SHAO Hui-he |
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| Institution: | Dept. of Automation, Shanghai Jiaotong Univ. , Shanghai 200240, China |
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| Abstract: | A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration. |
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| Keywords: | kernel independent component analysis (KICA) multivariate exponentially weighted moving average(MEWMA) nonlinear fault detection process monitoring fluid catalytic cracking unit (FCCU) process |
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