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Combined support vector novelty detection for multi-channel combustion data

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. We compare four novelty score combination mechani...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICNSC.2007.372828

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Institution:
University of Oxford
Department:
Oxford, MSD, NDORMS
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Publisher:
IEEE Publisher's website
Journal:
2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 Journal website
Pages:
495-500
Publication date:
2007-10-05
DOI:
URN:
uuid:18c4922f-f29e-4d25-9fef-a6d586148bfc
Source identifiers:
288497
Local pid:
pubs:288497
ISBN:
1-4244-1076-2

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