基于相关系数的多变量异常数据段的检测  

Multivariate Process Variables Abnormal Data Segments Detection Based on Correlation Coefficient

作　　者：庞向坤 黄越 王振 余彦 高嵩 PANG Xiang-kun;HUANG Yue;WANG Zhen;YU Yan;GAO Song(Shandong Electric Power Research Institute for State Grid Corporation of China,Jinan 250002 China;Beijing Institute of Collaborative Innovation,Beijing 100094,China;College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;College of Engineering,Peking University,Beijing 100871,China)

机构地区：[1]国网山东省电力公司电力科学研究院,山东济南250002 [2]北京协同创新研究院,北京100094 [3]山东科技大学电气与自动化工程学院,山东青岛266590 [4]北京大学工学院,北京100871

出　　处：《控制工程》2020年第1期194-200,共7页Control Engineering of China

基　　金：国家自然科学基金重点项目(61433001)

摘　　要：工业报警系统普遍存在报警过多的问题,为了获取过程变量的正常与异常历史数据段,从而设计工业报警系统的动态报警阈值,进行性能评估和优化,提出了一种基于过程变量之间的相关系数进行异常数据段检测的改进方法。该检测方法将相关系数和数据段长度作为分段依据,对过程变量的多元时间序列进行分段,采用Spearman秩相关系数获取变量之间的相关关系,从而检测出不符合正常趋势的异常数据段。仿真算例和工业案例表明该方法可真实地反映过程变量之间的相关关系,并准确检测出所存在的异常数据段。The historical normal and abnormal data sets of process variables are premises of assessing and optimizing alarm performance and designing dynamic alarm trip-points of industrial alarm system. This paper proposes an improved abnormal data detection method, which is based on the correlation coefficients between process variables. The main idea is to divide multivariate time series of process variables on the basis of correlation coefficient values and suitable length of data segments, obtain the mutual variation directions of process variables through Spearman rank correlation coefficient and corresponding hypothesis test, and detect abnormal data segments that have inconsistent variation directions with prior knowledge for normal conditions. Simulation examples and industrial case are provided to validate this method.

关 键 词：工业报警系统 异常数据段检测 相关系数 多元时间序列 

分 类 号：TQ35[化学工程]