NDLI: A dual-parameter optimization KPCA method for process fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Hongquan Jiang Xu Gao Zhiyong Gao Yunlong Li
Copyright Year	2015
Description	Author affiliation: State Key Lab. for Manuf. Syst. Eng., Xi'an Jiaotong Univ., Xi'an, China (Hongquan Jiang; Xu Gao; Zhiyong Gao; Yunlong Li)
Abstract	Kernel principal-component analysis (KPCA)-based methods have recently shown to be very effective for process fault diagnosis, and significant research has been done in recent years. However, the known fault datasets are not used while modeling and analyzing with KPCA, which results in low efficient fault detection. Another deficiency of traditional KPCA is lack of suitable selection criteria for choice of kernel parameters and the number of principal components, and some of fault detection rate can not meet the requirements or even some kind of fault can not be detected. In this paper, a kernel principal-component analysis (KPCA) method based on Dualparameter optimization is developed. Firstly, the effect of kernel parameters and the number of principal components to the performance of fault detection are analyzed based on traditional KPCA. Secondly, a Dual-parameter optimization for KPCA is introduced. For each type of fault, the effect of kernel parameters on the extraction of nonlinear features and effect of number of principal components on dimensionality reduction are both taken into account. A case study of a Tennessee Eastman (TE) system is provided to illustrate our work. The study results show that Dual-parameter optimization KPCA method can greatly improve the performance of fault detection over the popular KPCA-based methods.
Starting Page	1
Ending Page	7
File Size	400712
Page Count	7
File Format	PDF
e-ISBN	9781479967032
DOI	10.1109/RAMS.2015.7105134
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Optimization Principal component analysis Fault detection Feature extraction Monitoring Eigenvalues and eigenfunctions Process Fault Diagnosis Kernel Principal-Component Analysis-KPCA Parameter Optimization
Content Type	Text
Resource Type	Article

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