摘要
化工厂中一个小故障可能导致大事故,从而造成生命财产损失和环境破坏。为了防止小故障演变成大事故,化学工业需要有效的过程监控来及时检测故障和诊断故障原因。传统化工过程监控方法主元分析法(Principal Component Analysis, PCA)假设数据服从高斯分布,实践中有时并不满足该条件。此外,其使用方差、协方差捕捉数据非线性变化时,鲁棒性较差。本工作提出一种改进的主元分析法—基于约翰逊转换的鲁棒过程监控方法。首先引入约翰逊正态转换(Johnson Transformation)使过程数据服从高斯分布;其次使用鲁棒性强的斯皮尔曼相关系数(Spearman Correlation Coefficient)矩阵代替传统主元分析法的协方差矩阵提取特征向量,构造特征空间;最后将过程数据投影到特征空间,使用T2和SPE统计量实施过程监控。将此方法应用于TE过程故障案例,并与PCA和核主元分析法(Kernel Principal Component Analysis, KPCA)对比,验证了此方法的有效性。
A tiny fault in chemical plants is likely to cause an enormous accident possibly with heavy losses of personnel,property,and environment.Therefore,process monitoring is demanded to timely detect faults and identify fault variables,so as to avoid deterioration of tiny faults into accidents.Nowadaysprincipal component analysis (PCA) is the most widely used method in chemical process monitoring practice with its simplicity and effectiveness.However,it has some drawbacks.First,it roughly assumes process data as Gaussian distribution.But sometimes it is not satisfied.Furthermore,PCA uses variance and covariance (also called Pearson correlation coefficients) as criterion to choose principal components,however they are not robust in capturing nonlinear data variation.To alleviate these problems,an improved PCA-a Johnson transformation based robust method for process monitoring (JSPCA) was proposed in this work.First,Johnson transformation was introduced to make process data obey Gaussian distribution.Second,the Spearman correlation coefficient matrix instead of covariance matrix was established to extract principal components and span feature space.Finally,process data were projected into feature space where T;and SPE statistics were obtained for process monitoring.The proposed method had its fault detection ability tested in the benchmark TE process with comparison of PCA and KPCA.The results showed that the proposed method had higher fault detection rates than PCA and KPCA when using T;as detection indicator.However,the proposed method with SPE as detection indicator had higher false alarm rates than PCA and KPCA.As for fault diagnosis ability,the proposed method was tested against fault 5 and fault 10 of TE process and diagnoses fault variables more precisely than PCA and KPCA.The proposed method was better than PCA and KPCA and it was worth promoting.
作者
王骥
柳楠
胡明刚
田文德
Ji WANG;Nan LIU;Minggang HU;WendeTIAN(College of Chemical Engineering,Qingdao University of Science&Technology,Qingdao,Shandong 266042,China;Qingdao Nuocheng Chemical Safety Technology Co.,Ltd.,Qingdao,Shandong 266071,China)
出处
《过程工程学报》
CAS
CSCD
北大核心
2021年第12期1491-1502,共12页
The Chinese Journal of Process Engineering
基金
山东省重点研发项目(编号:2018YFJH0802)。
关键词
斯皮尔曼相关系数
TE过程
约翰逊转换
过程监控
Spearman correlation coefficient
TE process
Johnson transformation
process monitoring
