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.
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Clifton, L.A., Yin, H., Zhang, Y. (2006). Support Vector Machine in Novelty Detection for Multi-channel Combustion Data. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_122
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DOI: https://doi.org/10.1007/11760191_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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