Paper Title:
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.

  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, 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
$32.00
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