NDLI: A hybrid kernel PCA, hypersphere SVM and extreme learning machine approach for nonlinear process online fault detection

Content Provider	IEEE Xplore Digital Library
Author	Mengqi, N. Dong Jingjing Wang Tianzhen Gao Diju Han Jingang Benbouzid, M.E.H.
Copyright Year	2015
Description	Author affiliation: LBMS, Univ. of Brest, Brest, France (Benbouzid, M.E.H.) \|\| Dept. of Electr. Autom., Shanghai Maritime Univ., Shanghai, China (Mengqi, N.; Dong Jingjing; Wang Tianzhen; Gao Diju; Han Jingang)
Abstract	This paper presents a hybrid approach for online fault detection in nonlinear processes. To solve the possible monitoring difficulties caused by nonlinear characteristics of industrial process data, two applications of the Kernel Method: Hypersphere Support Vector Machine (HSSVM) and Kernel Principal Component Analysis (KPCA) are used as fault detection methods. On top of that, to obtain the adaptive models for online monitoring and fault detection in unsteady-stage conditions, instead of the static ones established by traditional HSSVM and KPCA, multiple methods are adopted, including Recursive KPCA, Adaptive Control Limit (ACL) and Online Sequential Extreme Learning Machine (OS-ELM), all of which update the detection model in real time with dynamically adjusting. The T2 control limit of Recursive KPCA, the classification hyperspheres of HSSVM and the single hidden layer feedforward network (SLFN) trained with OS-ELM work collaboratively in monitoring the real time process data to detect the possible faults. The proposed approach was tested and validated via a set of experimental data collected from a bearing test rig. Experimental results show that this approach is adequate for fault detection while meets the needs of real time performance.
Sponsorship	IEEE
Starting Page	002106
Ending Page	002111
File Size	369977
Page Count	6
File Format	PDF
e-ISBN	9781479917624
DOI	10.1109/IECON.2015.7392412
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Fault detection Kernel Support vector machines Principal component analysis Adaptation models Computational modeling Online fault detection KPCA HSSVM ELM Nonlinear process
Content Type	Text
Resource Type	Article

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