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第32届中国过程控制会议(CPCC2021)

作者： Wenzhe Zhang Youqing Wang College of Electrical Engineering and Automation Shandong University of Science and Technology

nonlinear and dynamic problems are very common in the industrial *** neural correlation analysis(ANCA) can monitor the nonlinear processes *** with dynamic canonical correlation analysis(DCCA),multistep canonical correlation analysis(MS-CCA) can effectively monitor the dynamic process by considering the noise information in the original *** paper draws on the dynamic model of MS-CCA and utilizes ANCA's ability to deal with nonlinear dynamic problems in industrial processes to propose a method called the nonlinear dynamic process monitoring based on multistep artificial neural correlation analysis.A continuous stirred tank reactor(CSTR) is used to verify the *** show that the proposed method can effectively detect the abnormality in the nonlinear dynamic industrial processes.

关键词： artificial neural correlation analysis(ANCA) multistep canonical correlation analysis(MS-CCA) nonlinear dynamic process monitoring fault detection

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Canonical Variate nonlinear Principal Component Analysis for monitoring nonlinear dynamic processes

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2022年第1期55卷 29-37页

作者： Shang, Liangliang Qiu, Aibing Xu, Peng Yu, Feng Nantong Univ Sch Elect Engn Nantong 226019 Jiangsu Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

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 Q(c) 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.

关键词： Canonical Variate Explicit Polynomial Mapping nonlinear dynamic process monitoring Combined Statistic

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Blast Furnace Ironmaking process Fault Detection Using Canonical Variate Analysis and Support Vector Data Description 34

Blast Furnace Ironmaking Process Fault Detection Using Canon...

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34th Chinese Control and Decision Conference (CCDC)

作者： Wang, Xuemei Wu, Ping Pan, Haipeng Zhejiang Sci Tech Univ Sch Mech Engn & Automat Hangzhou 310018 Peoples R China

ISBN: (纸本)9781665478960

Anomalies detection plays a vital role in ensuring the stability and smoothness of the blast furnace ironmaking process. Due to its complexity, data-driven fault detection has become a hot topic for the blast furnace ironmaking process. To monitor the process of the blast furnace, this paper proposes a fault detection method based on Canonical Variate Analysis and Support Vector Data Description (CVA-SVDD). CVA is a state space-based monitoring tool that is effective in dealing with process dynamics. SVDD is a data domain description method, also known as a one-classification method, which can be used for outlier detection and has the advantage of solving the problem of describing nonlinear and non-Gaussian distributed data. Specifically, a CVA model is first constructed for the process variables to generate a series of feature vectors, and then the features are further monitored using SVDD, which is used to fit the data into a hypersphere so that its radius can be used as a health indicator in fault detection. A real blast furnace ironmaking process is employed to verify the effectiveness of the proposed method.

关键词： Canonical Variate Analysis Support Vector Data Description Blast Furnace Ironmaking process nonlinear dynamic process monitoring

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Blast Furnace Ironmaking process Fault Detection Using Canonical Variate Analysis and Support Vector Data Description

Blast Furnace Ironmaking Process Fault Detection Using Canon...

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第34届中国控制与决策会议

作者： Xuemei Wang Ping Wu Haipeng Pan School of Mechanical Engineering & Automation Zhejiang Sci-Tech University

Anomalies detection plays a vital role in ensuring the stability and smoothness of the blast furnace ironmaking *** to its complexity,data-driven fault detection has become a hot topic for the blast furnace ironmaking *** monitor the process of the blast furnace,this paper proposes a fault detection method based on Canonical Variate Analysis and Support Vector Data Description(CVA-SVDD).CVA is a state space-based monitoring tool that is effective in dealing with process *** is a data domain description method,also known as a oneclassification method,which can be used for outlier detection and has the advantage of solving the problem of describing nonlinear and non-Gaussian distributed ***,a CVA model is first constructed for the process variables to generate a series of feature vectors,and then the features are further monitored using SVDD,which is used to fit the data into a hypersphere so that its radius can be used as a health indicator in fault detection.A real blast furnace ironmaking process is employed to verify the effectiveness of the proposed method.

关键词： Canonical Variate Analysis Support Vector Data Description Blast Furnace Ironmaking process nonlinear dynamic process monitoring

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