Fault Diagnosis of Industrial Process Based on FDKICA-PCA

Fault Diagnosis of Industrial Process Based on FDKICA-PCA

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 con...

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Bibliographic Details
Main Authors: ZHANG Jing, ZHU Fei-fei, LIU Jia-xing, WANG Jiang-tao
Format: Article
Language:zho
Published: Harbin University of Science and Technology Publications 2018-12-01
Series:Journal of Harbin University of Science and Technology
Subjects:
fault diagnosis
wavelet packets
principal component analysis
kernel independent component analysis
ar model
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1615
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Summary: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.
ISSN:1007-2683

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