NDLI: Nonlinear Process Monitors Method Based on Kernel Function and PNN

Content Provider	IEEE Xplore Digital Library
Author	Bo Cuimei Li Jiin Lu Aijing Zhuang Guangming
Copyright Year	2007
Description	Author affiliation: Nanjing Univ. of Technol., Nanjing (Bo Cuimei; Li Jiin; Lu Aijing; Zhuang Guangming)
Abstract	Kernel PCA can efficiently compute principal components in high-dimensional feature spaces by means of nonlinear kernel functions. Therefore, the nonlinear problems are translated into the linear ones in the space high-dimension feature space. Although it has been proved that KPCA is superior to linear PCA for fault detection, the problem of fault identification theoretically has yet been a puzzle. A new fault detection and identification method based on the gradient arithmetic of kernel function and probabilistic neural network (PNN) for nonlinear system is developed. The gradient arithmetic of kernel function is used to extract the main features of faults firstly. Then, probabilistic neural network is used to identify the fault variables. To demonstrate the performance, the proposed method is applied to Tennessee Eastman processes. The simulation results under 15 fault modes of TE process show that the proposed method effectively identifies the source of various types of faults.
Starting Page	511
Ending Page	515
File Size	571342
Page Count	5
File Format	PDF
ISBN	9787811240559
DOI	10.1109/CHICC.2006.4347489
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation probabilistic neural network gradient arithmetic of kernel function Process monitor Tellurium Fault diagnosis Space technology Fault detection Neural networks Kernel Nonlinear systems Tennessee Eastman proces processes Principal component analysis Arithmetic
Content Type	Text
Resource Type	Article

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