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Combined Support Vector Novelty Detection for Multi-channel Combustion Data
2007
2007 IEEE International Conference on Networking, Sensing and Control
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Multi-channel combustion data, consisting of gas pressure and two combustion chamber luminosity measurements, are investigated in the prediction of combustion instability. Wavelet analysis is used for feature extraction. A SVM approach is applied for novelty detection and the construction of a model of normal system operation. Novelty scores generated by classifiers from different channels are combined to give a final decision of data novelty. We compare four novelty score combination
doi:10.1109/icnsc.2007.372828
dblp:conf/icnsc/CliftonYCZ07
fatcat:4rfzmsk4qzbg7meyussvd4hmbu