Multimode Monitoring of Oxy-gas Combustion through Flame Imaging, Principal Component Analysis and Kernel Support Vector Machine

Bai, Xiaojing and Hossain, Md. Moinul and Lu, Gang and Yan, Yong and Liu, Shi (2016) Multimode Monitoring of Oxy-gas Combustion through Flame Imaging, Principal Component Analysis and Kernel Support Vector Machine. Combustion Science and Technology, 189 (5). pp. 776-792. ISSN 0010-2202. (doi:https://doi.org/10.1080/00102202.2016.1250749) (Full text available)

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http://dx.doi.org/10.1080/00102202.2016.1250749

Abstract

This paper presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis and kernel support vector machine (PCA-KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through Watershed Transform method. The weighted color and texture features of the diffused and premixed regions are extracted and projected into two subspaces using the PCA to reduce the data dimensions and noises. The multi-class KSVM model is finally built based on the flame features in the principal component subspace to identify the operation condition. Two classic multivariate statistic indices, i.e. Hotelling’s T2 and squared prediction error (SPE), are used to assess the normal and abnormal states for the corresponding operation condition. The experimental results obtained on a lab-scale oxy-gas rig show that the weighted color and texture features of the defined diffused and premixed regions are effective for detecting the combustion state and that the proposed PCA-KSVM model is feasible and effective to monitor a combustion process under variable operation conditions.

Item Type: Article
Uncontrolled keywords: Combustion stability, Flame imaging, Kernel support vector machine, Principal components analysis, Multimode process monitoring
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Gang Lu
Date Deposited: 20 Oct 2016 11:06 UTC
Last Modified: 05 Dec 2018 14:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57974 (The current URI for this page, for reference purposes)
Hossain, Md. Moinul: https://orcid.org/0000-0003-4184-2397
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