FAULT DIAGNOSIS OF BATCH PROCESSES RELEASE USING PCA CONTRIBUTION PLOTS AS FAULT SIGNATURES

摘要：

The diagnosis of qualitative variables in certain types of batch processes requires time to measure the variables and obtain the final result of the released product. With principal component analysis (PCA) any abnormal behavior of the process can be detected. This study proposes a method that uses contribution plots as fault signatures (FS) on the different stages and variables of the process to diagnose the quality variables from the released product. Therefore, in a product resulting from the abnormal behavior of a process the qualitative variables that need to be measured could be obtained through the quantitative variables of the process by classifying the FS with a knowledge model from a fault signature database (FSD) extracted with a classification algorithm. The method is tested in a biological nutrient removal (BNR) sequencing batch reactor (SBR) for wastewater treatment to diagnose qualitative variables of the process: ammonium (NH+ 4 ), nitrates (NO. 2 orNO. 3 ) and phosphate (PO3. 4 ).

关键词： Batch Processes Contribution Plots Data Mining Classification Algorithms Principal Component Analysis

作者: Alberto Wong Ramirez Joan Colomer Llin`as

作者单位: Control Engineering and Intelligent System Group (eXiT), University of Girona, Campus Montilivi, Girona, Spain

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2109-2114

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）