基于LTSA和联合指标的非高斯过程监控方法及应用  

LTSA and combined index based non-Gaussian process monitoring and application

作　　者：杨正永 王昕[2] 王振雷[1] 

机构地区：[1]化工过程先进控制和优化技术教育部重点实验室(华东理工大学),上海200237 [2]上海交通大学电工与电子技术中心,上海200240

出　　处：《化工学报》2015年第4期1370-1379,共10页CIESC Journal

基　　金：国家自然科学基金项目(61134007);国家高技术研究发展计划项目(2013AA040701);上海市科技攻关项目(12dz1125100);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资助项目(PAL-N201404)~~

摘　　要：很多实际工业过程数据都具有高维、非线性且不严格服从高斯分布等特点。为处理数据维数高且是高斯分布和非高斯分布的混合体等问题,实现高效的过程监控,提出了一种基于LTSA和联合指标的非高斯过程监控方法。首先采用局部切空间排列(LTSA)算法从正常样本数据中提取低维子流形以实现维数约减;然后基于非高斯-高斯两步策略建立统计模型并得到非高斯统计量和高斯统计量,再对其进行加权得到新的统计量以实现对过程的监控;最后将该方法应用于田纳西-伊斯曼标准测试平台和实际乙烯裂解炉的过程监控,说明了所提方法的有效性。Many industrial process variables have characteristics of high-dimension and not strictly obeying the Gaussian distribution. A method was proposed to solve these problems of the industrial process. The method was based on LTSA algorithm and combined index to improve monitoring performance. Firstly, the local tangent space alignment（LTSA） algorithm was used to get the sub-manifold of low dimension from the normal sample data to achieve dimensionality reduction. The two-step strategy was used to get the new statistical model. Then, the non-Gaussian statistical value and the Gaussian statistical value were constructed. Therefore, the new statistical value weighted by these two statistical values was intended to achieve monitoring of the process. Finally, the proposed method was applied to the Tennessee Eastman（TE） process and the ethylene cracking furnace to demonstrate its effectiveness.

关 键 词：算法 集成 系统工程 非线性 非高斯 联合指标 局部切空间排列算法 田纳西-伊斯曼过程 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]