An improved fault detection algorithm based on wavelet analysis and kernel principal component analysis

Author

Chen, Liang ; Yu, Yang ; Luo, Jie ; Zhao, Yawei

Author_Institution

Fac. of Inf. Sci. & Eng., Shenyang Ligong Univ., Shenyang, China

fYear

2010

fDate

26-28 May 2010

Firstpage

1723

Lastpage

1726

Abstract

Original signal is decomposed by wavelet in different scales, the wavelet decomposition coefficients of the real signal are held, and the wavelet decomposition coefficients of the noise are eliminated, then the signal is reconstructed by inverse wavelet transform. Kernel PCA can eliminate the relativity of variables and extract the fault information better, the feature information of the pretreatment datum is obtained by KPCA, and the performance of fault detection is improved.

Keywords

principal component analysis; process control; wavelet transforms; fault detection; fault information; inverse wavelet transform; kernel principal component analysis; wavelet analysis; wavelet decomposition; Algorithm design and analysis; Fault detection; Kernel; Matrix decomposition; Principal component analysis; Signal mapping; Signal processing; Valves; Wavelet analysis; Wavelet transforms; fault detection; kernel principal component analysis; tennessee-eastman process; wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498444

Filename

5498444