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Mixture Discriminant Monitoring: A Hybrid Method for Statistical Process Monitoring and Fault Diagnosis/Isolation

Huang, C-C, Chen, T and Yao, Y (2013) Mixture Discriminant Monitoring: A Hybrid Method for Statistical Process Monitoring and Fault Diagnosis/Isolation Industrial and Engineering Chemistry Research, 52 (31). pp. 10720-10731.

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Abstract

To better utilize historical process data from faulty operations, supervised learning methods, such as Fisher discriminant analysis (FDA), have been adopted in process monitoring. However, such methods can only separate known faults from normal operations, and they have no means to deal with unknown faults. In addition, most of these methods are not designed for handling non-Gaussian distributed data; however, non-Gaussianity is frequently observed in industrial processes. In this paper, a hybrid multivariate approach named mixture discriminant monitoring (MDM) was proposed, in which supervised learning and statistical process control (SPC) charting techniques are integrated. MDM is capable of solving both of the above problems simultaneously during online process monitoring. Then, for known faults, a root-cause diagnosis can be automatically achieved, while for unknown faults, abnormal variables can be isolated through missing variable analysis. MDM was used on the benchmark Tennessee Eastman (TE) process, and the results showed the capability of the proposed approach.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
AuthorsEmailORCID
Huang, C-CUNSPECIFIEDUNSPECIFIED
Chen, TUNSPECIFIEDUNSPECIFIED
Yao, YUNSPECIFIEDUNSPECIFIED
Date : 7 August 2013
Identification Number : 10.1021/ie400418c
Uncontrolled Keywords : Science & Technology, Technology, Engineering, Chemical, Engineering, ENGINEERING, CHEMICAL, PRINCIPAL COMPONENT ANALYSIS, MULTIPLE OPERATING MODES, DENSITY-ESTIMATION, DIAGNOSIS, PLS, PCA
Related URLs :
Additional Information : This document is the Accepted Manuscript version of a published work that appeared in final form in Industrial and Engineering Chemistry Research, copyright © American Chemical Society after peer review and technical editing by the publisher.To access the final edited and published work see 10.1021/ie400418c.
Depositing User : Symplectic Elements
Date Deposited : 24 Nov 2015 14:51
Last Modified : 24 Nov 2015 14:51
URI: http://epubs.surrey.ac.uk/id/eprint/809145

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