An Improved KPCA Method of Fault Detection Based on Wavelet Denoising

Abstract:

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For complicated nonlinear systems, the data inevitably have noise, random disturbance, Traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix K for fault detection with large sample sets. So an improved KPCA method based on wavelet denoising is proposed. First, wavelet denoising method is used for data processing, then the improved KPCA method can reduce calculational complexity of fault detection. The proposed method is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.

Info:

Periodical:

Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng

Pages:

1427-1432

DOI:

10.4028/www.scientific.net/KEM.467-469.1427

Citation:

X. Q. Zhao and Z. M. Li, "An Improved KPCA Method of Fault Detection Based on Wavelet Denoising", Key Engineering Materials, Vols. 467-469, pp. 1427-1432, 2011

Online since:

February 2011

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Price:

$35.00

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