Title :
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
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;
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
DOI :
10.1109/CCDC.2010.5498444
