Abstract
In this paper, we propose a method to detect boiler tube leakage using a genetic algorithm (GA)-like method and support vector machines (SVM). The GA-like method allows for selection of significant features, and the SVM detects a leak in boiler tubes using the selected features. Experimental results indicate that the proposed method outperforms a state-of-the-art principle component analysis (PCA) method in leakage detection.
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Acknowledgement
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161120100350, No. 20181510102160, No. 20162220100050).
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Kim, YH., Kim, J., Kim, JM. (2020). Leakage Detection of a Boiler Tube Using a Genetic Algorithm-like Method and Support Vector Machines. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_9
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DOI: https://doi.org/10.1007/978-3-030-17065-3_9
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