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Nonlinear process monitoring and fault isolation using extended maximum variance unfolding

Liu, Y-J, Yao, Y and Chen, T (2014) Nonlinear process monitoring and fault isolation using extended maximum variance unfolding Journal of Process Control, 24 (6). pp. 880-891.

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Abstract

Kernel principal component analysis (KPCA) has become a popular technique for process monitoring, owing to its capability of handling nonlinearity. Nevertheless, KPCA suffers from two major disadvantages. First, the underlying manifold structure of data is not considered in process modeling. Second, the selection of kernel parameters is problematic. To avoid such deficiencies, a manifold learning technique named maximum variance unfolding (MVU) is considered as an alternative. However, such method is merely able to deal with the training data, but has no means to handle new samples. Therefore, MVU cannot be applied to process monitoring directly. In this paper, an extended MVU (EMVU) method is proposed, extending the utilization of MVU to new samples by approximating the nonlinear mapping between the input space and the output space with a Gaussian process model. Consequently, EMVU is suitable to nonlinear process monitoring. A cross-validation algorithm is designed to determine the dimensionality of the EMVU output space. For online monitoring, three different types of monitoring indices are developed, including squared prediction error (SPE), Hotelling-T, and the prediction variance of the outputs. In addition, a fault isolation algorithm based on missing data analysis is designed for EMVU to identify the variables contributing most to the faults. The effectiveness of the proposed methods is verified by the case studies on a numerical simulation and the benchmark Tennessee Eastman (TE) process. © 2014 Elsevier Ltd. All rights reserved.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
AuthorsEmailORCID
Liu, Y-JUNSPECIFIEDUNSPECIFIED
Yao, YUNSPECIFIEDUNSPECIFIED
Chen, TUNSPECIFIEDUNSPECIFIED
Date : June 2014
Identification Number : 10.1016/j.jprocont.2014.04.004
Additional Information : NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Process Control. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Process Control, 24(6), June 2014, DOI 10.1016/j.jprocont.2014.04.004.
Depositing User : Symplectic Elements
Date Deposited : 08 Aug 2014 09:14
Last Modified : 03 Mar 2015 11:49
URI: http://epubs.surrey.ac.uk/id/eprint/805797

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