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Erschienen in:
2012 | OriginalPaper | Buchkapitel
A Fault Detection Algorithm Based on Wavelet Denoising and KPCA
verfasst von : Xiaoqiang Zhao, Xinming Wang
Erschienen in: Advances in Future Computer and Control Systems
Verlag: Springer Berlin Heidelberg
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Data of nonlinear chemical industry process have characterics of containing noises and random disturbances. An improved fault detection method based on wavelet denoising and kernel principal component analysis (KPCA) method is developed, it can not only denoise and anti-disturb, but also can transform nonlinear problems in the input space into linear problems in the feature space. So this can solves the poor performances of principal component analysis (PCA) method in nonlinear problems. The proposed method is applied to Tennessee Eastman (TE) process. The simulation results verify that the proposed method is superior to PCA method obviously in fault detection.