Abstract
Air preheater is the important heat exchanger in power plant units. Recombustion accident can be caused by inadequacy combustion of fuel or badly heat-dispersed condition aroused by low air or gas velocity after boiler outage. In the paper, discriminant models of 3 pairs of fire status have been built based on Least Square Support Vector Machines (LS-SVMs) for two kinds of kernel functions. Utilizing polynomial and RBF kernel, the hyperparameters of classifiers were tuned with Leave-one-out(LOO) cross-validation. Receiver Operating Characteristic(ROC) curve comparison shows that LS-SVMs classifiers are able to learn quite well from the raw data samples. Experiment results show that SVMs has good classification and generalization ability and RBF kernel function has more accurate than polynomial kernel function for this problem from the area under the ROC curve (AUC) values of two kernel functions.
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© 2004 Springer-Verlag Berlin Heidelberg
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Han, L., Ding, L., Yu, J., Li, Q., Liang, Y. (2004). Power Plant Boiler Air Preheater Hot Spots Detection System Based on Least Square Support Vector Machines. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_98
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DOI: https://doi.org/10.1007/978-3-540-28647-9_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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