A Fault Detection Algorithm Based on Wavelet Denoising and KPCA

	A Fault Detection Algorithm Based on Wavelet Denoising and KPCA
	Zhao, Xiaoqiang 1; Wang, Xinming 2
	2012
关键词	Fault detection KPCA Wavelet denoising TE processes
卷号	159
页码	311-+
英文摘要	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.
会议录	ADVANCES IN FUTURE COMPUTER AND CONTROL SYSTEMS, VOL 1
会议录出版者	SPRINGER-VERLAG BERLIN
会议录出版地	HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
语种	英语
资助项目	Master Tutor Project of Education Department of Gansu Province[1003ZTC085]
WOS研究方向	Computer Science
WOS记录号	WOS:000310547200046
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/37230]
专题	电气工程与信息工程学院
通讯作者	Zhao, Xiaoqiang
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China 2.Gansu Province Med Sci Inst, Lanzhou, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,Wang, Xinming. A Fault Detection Algorithm Based on Wavelet Denoising and KPCA[C]. 见:.