基于差分分段PCA的多模态过程故障监测  

Fault Detection of Multi-mode Process Using Segmented PCA Based on Differential Transform

作　　者：谭帅[1] 王福利[1,2] 常玉清[1,2] 王姝[1] 周贺 

机构地区：[1]东北大学信息科学与工程学院,沈阳110004 [2]东北大学流程工业综合自动化教育部重点实验室,沈阳110004 [3]辽河油田钻采工艺研究院中心试验所,盘锦124000

出　　处：《自动化学报》2010年第11期1626-1636,共11页Acta Automatica Sinica

基　　金：国家重点基础研究发展计划(973计划)(2009CB320601);国家自然科学基金(60774068)资助~~

摘　　要：多模态的故障监测是一个复杂的问题,既需要考虑稳定模态下的故障监测,也需要考虑不同模态间的过渡故障监测.不同稳定模态下的数据具有不同的相关关系,对每个稳定模态需要建立不同的稳定模态模型.当稳定生产模态发生改变时,生产过程进入过渡模态,需要考虑过渡变量相关关系的变化.本文通过对过渡数据差分,得到变量相对变化信息.利用主成分分析(Principal component analysis,PCA)分段对差分变量的相关特性进行分析,提取相对变化的特征.最后以实际连续退火机组生产线为背景,用基于差分分段PCA的多模态方法对多模态过程进行故障监测,发现算法很好地反映了实际过渡过程机理,验证了算法的有效性.Fault detection for multi-mode process is a complicated problem,as the fault detection for both steady mode and transition mode should be taken into consideration. Different modes are needed for different steady modes because different relations of variables are contained in each mode model. Transition mode is a dynamic process occurring when production changes operating mode. The dynamic characteristic refiects not only the changing variables but also the changing relation of variables. The relative change information can be obtained by differential transform of transition data. Principle components can be extracted by analyzing correlation of differential variables using principal component analysis （PCA）. At last,segmented modeling with PCA method is used to monitor multi-mode process of continuous annealing line. The algorithm reflects transition process well and is proved to be efficient.

关 键 词：差分矩阵 分段建模 多模态 故障监测 连续退火机组 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]