Combined support vector novelty detection for multi-channel combustion data

Lei A. Clifton; Hujun Yin; David A. Clifton; Yang Zhang

doi:10.1109/ICNSC.2007.372828

Combined support vector novelty detection for multi-channel combustion data

Lei A. Clifton, Hujun Yin, David A. Clifton, Yang Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 mechanisms, and illustrate their complementary relationship in assessing data novelty. © 2007 IEEE.

Original language	English
Title of host publication	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07\|IEEE Int. Conf. Netw. Sens. Control
Publisher	IEEE
Pages	495-500
Number of pages	5
ISBN (Print)	1424410762, 9781424410767
DOIs	https://doi.org/10.1109/ICNSC.2007.372828
Publication status	Published - 2007
Event	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 - London Duration: 1 Jul 2007 → …

Conference

Conference	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
City	London
Period	1/07/07 → …

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10.1109/ICNSC.2007.372828

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@inproceedings{a67cd74bf842443bac51d3609e58edbb,

title = "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 mechanisms, and illustrate their complementary relationship in assessing data novelty. {\textcopyright} 2007 IEEE.",

author = "Clifton, {Lei A.} and Hujun Yin and Clifton, {David A.} and Yang Zhang",

year = "2007",

doi = "10.1109/ICNSC.2007.372828",

language = "English",

isbn = "1424410762",

pages = "495--500",

booktitle = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07|IEEE Int. Conf. Netw. Sens. Control",

publisher = "IEEE",

address = "United States",

note = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 ; Conference date: 01-07-2007",

}

Clifton, LA, Yin, H, Clifton, DA & Zhang, Y 2007, Combined support vector novelty detection for multi-channel combustion data. in 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07|IEEE Int. Conf. Netw. Sens. Control. IEEE, pp. 495-500, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, London, 1/07/07. https://doi.org/10.1109/ICNSC.2007.372828

Combined support vector novelty detection for multi-channel combustion data. / Clifton, Lei A.; Yin, Hujun; Clifton, David A. et al.
2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07|IEEE Int. Conf. Netw. Sens. Control. IEEE, 2007. p. 495-500.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Combined support vector novelty detection for multi-channel combustion data

AU - Clifton, Lei A.

AU - Yin, Hujun

AU - Clifton, David A.

AU - Zhang, Yang

PY - 2007

Y1 - 2007

N2 - 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. © 2007 IEEE.

AB - 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. © 2007 IEEE.

U2 - 10.1109/ICNSC.2007.372828

DO - 10.1109/ICNSC.2007.372828

M3 - Conference contribution

SN - 1424410762

SN - 9781424410767

SP - 495

EP - 500

BT - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07|IEEE Int. Conf. Netw. Sens. Control

PB - IEEE

T2 - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Y2 - 1 July 2007

ER -

Combined support vector novelty detection for multi-channel combustion data

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