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Combined Support Vector Novelty Detection for Multi-channel Combustion Data

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4 Author(s)
Clifton, L.A. ; Manchester Univ., Manchester ; Hujun Yin ; Clifton, D.A. ; Yang Zhang

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 mechanisms, and illustrate their complementary relationship in assessing data novelty.

Published in:

Networking, Sensing and Control, 2007 IEEE International Conference on

Date of Conference:

15-17 April 2007