NDLI: Nonlinear dynamic process monitoring based on DLLE-SVDD

Content Provider	IEEE Xplore Digital Library
Author	Yuxin Ma Mengling Wang Hongbo Shi
Copyright Year	2012
Description	Author affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai, China (Yuxin Ma; Mengling Wang; Hongbo Shi)
Abstract	A novel process monitoring method for dynamic nonlinear industrial processes is proposed by combining dynamic Locally Linear Embedding(DLLE) with Support Vector Data Description(SVDD). Firstly, the data matrix is augmented taking correlation of the samples into consideration. Then, LLE manifold learning algorithm is performed for nonlinear dimensionality reduction and feature extraction. The mapping matrix from data space to feature space was calculated by using local linear regression which guarantees the real-time property. Next, in order to avoid the influence of noise and disturbance on the traditional statistics, the fault detection model is obtained based on SVDD in the feature space, in which a corresponding monitoring index and its control limit are determined. Finally, the feasibility and efficiency of the proposed method are shown through the TE process.
Starting Page	3131
Ending Page	3136
File Size	183269
Page Count	6
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6358410
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monitoring Indexes Mathematical model Manifolds Process control Support vector machines Fault detection Locally Linear Embedding Support Vector Data Description
Content Type	Text
Resource Type	Article

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