Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy  

作　　者：Guozhu Wang Jianchang Liu Yuan Li Cheng Zhang 

机构地区：[1]College of information Science and Engineering Northeastern University, Shenyang 110819, Chino [2]State Key Laboratory of Synthetical Automationfor Process Industries, Northeastern University, Shenyang 110819, China [3]Information Engineering school,Shenyang University of Chemical Technology, Shenyang 110142, China

出　　处：《Chinese Journal of Chemical Engineering》2016年第7期869-880,共12页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61374137,61490701,61174119);the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds(2013ZCX02-03)

摘　　要：Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal compo- nent analysis （PPCA） method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model （each direction of principal components）, but also improves the monitoring performance through the combination of local monitoring results. Then, a var- iable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effec- tiveness of the proposed process monitoring and fault variable identification schemes is verified through a nu- merical example and TE chemical process.

关 键 词：Fault detectionFault identificationProcess monitoringPartitioning PCAVariable reasoning strategy 

分 类 号：TQ056[化学工程]