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

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.