An Improved KPCA Method of Fault Detection Based on Wavelet Denoising

Xiao Qiang Zhao; Zhan Ming Li

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

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 467-469An Improved KPCA Method of Fault Detection Based...

An Improved KPCA Method of Fault Detection Based on Wavelet Denoising

Abstract:

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.

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Periodical:

Key Engineering Materials (Volumes 467-469)

Pages:

1427-1432

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.467-469.1427

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Online since:

February 2011

Authors:

Xiao Qiang Zhao, Zhan Ming Li

Keywords:

Fault Detection, Kernel Principal Component Analysis (KPCA), Tennessee-Eastman Process, Wavelet Denoising

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