Multimode monitoring of oxy-gas combustion through flame imaging, principal component analysis, and kernel support vector machine

Xiaojing Bai, Gang Lu, Md Moinul Hossain, Yong Yan, Shi-Xia Liu

Research output: Contribution to journalArticle

4 Citations (Scopus)


This article presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis (PCA), and kernel support vector machine (KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through the 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, for example, Hotelling’s T2 and squared prediction error, 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.

Original languageEnglish
Pages (from-to)776-792
Number of pages17
JournalCombustion Science and Technology
Issue number5
Early online date8 Nov 2016
Publication statusPublished - 4 May 2017
Externally publishedYes



  • combustion stability
  • flame imaging
  • kernel support vector machine
  • multimode process monitoring
  • principal components analysis
  • oxy-gas fired conditions

Cite this