Support vector machine in novelty detection for multi-channel combustion data

Authors:
Lei A. Clifton;Hujun Yin;Yang Zhang
Affiliations:
School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK;School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK;School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, UK
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

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. Comparisons between the proposed SVM method and a GMM approach show that earlier identification of combustion instability, and greater distinction between stable and unstable data classes, are achieved with the proposed SVM approach.