| 基于多块MICA-PCA的全流程过程监控方法 |
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| 引用本文： | 王振雷,江伟,王昕. 基于多块MICA-PCA的全流程过程监控方法[J]. 控制与决策, 2018, 33(2): 269-274 |
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| 作者姓名： | 王振雷 江伟 王昕 |
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| 作者单位： | 华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海200237,华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海200237,上海交通大学电工与电子技术中心，上海200240 |
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| 基金项目： | 国家自然科学基金重点项目(61134007)；国家自然科学基金青年项目(61403141)；上海市“科技创新行动计划”研发平台建设项目(13DZ2295300)；上海市自然科学基金项目(14ZR1421800)；流程工业综合自动化国家重点实验室开放课题基金项目(PAL-N201404). |
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| 摘 要： | 多块策略广泛应用于全流程过程监控领域,以解决变量关系复杂性较高的问题,但传统分块方法得到的子块数据存在高斯与非高斯混合分布问题,影响过程监控的效果.为此,提出一种基于多块MICA-PCA的过程监控方法.首先采用Jarque-Bera(J-B)检测方法对原始数据进行高斯与非高斯分块;然后利用Hellinger距离(HD)方法获得高斯与非高斯子块,通过对高斯与非高斯子块采用不同的建模和诊断方法,提高监控效果;最后将该方法应用于田纳西-伊斯曼(TE)过程的监控中,以验证所提出方法的有效性.
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| 关 键 词： | 多块 全流程 主元分析 非高斯 |
Plant-wide process monitoring based on multiblock MICA-PCA |
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| WANG Zhen-lei,JIANG Wei and WANG Xin. Plant-wide process monitoring based on multiblock MICA-PCA[J]. Control and Decision, 2018, 33(2): 269-274 |
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| Authors: | WANG Zhen-lei JIANG Wei WANG Xin |
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| Affiliation: | Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai200237,China,Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai200237,China and Center of Electrical & Electronic Technology, Shanghai Jiaotong University, Shanghai200240,China |
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| Abstract: | The multiblock strategy is widely used for plant-wide process monitoring, to capture the relations between complex process variables of the plant-wide process, however, the sub-block data obtained from the traditional multiblock method still has the problem of non-Gaussian and Gaussian mixture distribution, which influences the performance of process monitoring. Therefore, a plant-wide process monitoring method based on multiblock MICA-PCA is proposed to improve the process monitoring performance. Firstly, the measured variables are automatically divided into non-Gaussian block and Gaussian block through the Jarque-Bera(J-B) test method, the non-Gaussian block and Gaussian block are divided into non-Gaussian sub-blocks and Gaussian sub-blocks through the Hellinger Distance(HD) method. By using different modeling and diagnosis methods for non-Gaussian sub-blocks and Gaussian sub-blocks, the monitoring effect is improved. Finally, the proposed method is applied to monitor the Tennessee-Eastman(TE) process, which shows its effectiveness. |
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