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 Fault Diagnosis of Industrial Process Based on FDKICAPCA(PDF)


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 Fault Diagnosis of Industrial Process Based on FDKICAPCA
 ZHANG Jing1ZHU Feifei1LIU Jiaxing1WANG Jiangtao2
 1.College of Automation,Harbin University of Science of Technology,Harbin 150080,China;2.School of Computer Science and Software Engineering,East China Normal University,Shanghai 200062,China)
 Keywords:fault diagnosis wavelet packets principal component analysis kernel independent component analysisAR model
 Abstract:Because the dynamic characteristics of autocorrelation and lag correlation in production process are neglected in fault diagnosis,Kernel Independent Component AnalysisPrincipal Component Analysis (KICAPCA) is very poor in detecting small and gradual faults because of lacking available variable contribution analysis.In this paper, a dynamic kernel independent component analysis (KICAPCA) fault diagnosis method based on wavelet packet filtering is proposed.This method integrates wavelet packet filtering theory and AR model prediction data characteristics into KICAPCA to extract the feature information of process variable autocorrelation and lagrelated .In this paper, KICAPCA algorithm is used to extract the independent components and principal components of process variables to determine the control limits of three monitoring indicators T2, SPE,I2.Nonlinear contribution graph is used for fault diagnosis, and the advantage of FDKICAPCA method is verified by simulation results of Tennessee process.


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Last Update: 2019-03-21