基于LTSA-Greedy-SVDD的过程监控  

Process Monitoring Based on LTSA-Greedy-SVDD

作　　者：杨正永 王昕[2] 王振雷[1] 

机构地区：[1]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 [2]上海交通大学电工与电子技术中心,上海200240

出　　处：《华东理工大学学报（自然科学版）》2014年第3期343-348,共6页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61134007);国家863计划(2013AA040701);上海市科技攻关项目(12dz1125100);"十二五"国家科技支撑计划(2012BAF05B00);上海重点学科建设项目(B504);流程工业综合自动化国家重点实验室开放课题基金

摘　　要：为解决实际工业过程中的非线性和非高斯问题,实现有效的过程监控,提出了一种基于局部切空间排列算法的过程监控方法。首先运用局部切空间排列算法对标准化后的正常样本数据提取出低维子流形以实现维数约减。之后利用Greedy方法提取特征样本以支持向量数据描述方法建立监控模型,最后采用相应统计量进行过程监控。以田纳西伊斯曼(TE)模型为仿真平台,仿真结果说明了该方法的有效性。There are some characteristics such as non-linearity and non-Gaussian in real industrial process data. Aiming at these problems, a process monitoring method based on the local tangent space alignment is proposed. Firstly, the local tangent space alignment algorithm is used to get the sub-manifold of low dimension from the normalized normal sample data such that the dimension reduction can be achieved. Then, Greedy method is used to extract feature sample to establish the monitoring model by support vector data description. Finally, the corresponding statistic is used for process monitoring. The simulation is made on the TE model, whose results illustrate the effectiveness of the proposed method.

关 键 词：非线性 局部切空间排列(LTSA)算法 Greedy方法 支持向量数据描述 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]