Abstract
The detection of leaks in circulating fluidized bed boiler tubes is an important issue in thermal power plants, because leaks lead to enormous economic and social losses. To address this issue, a time-based maintenance (TBM) method has been employed in power plants, but it cannot detect leakage of a tube during operation. Instead, a condition-based maintenance (CBM) method is required to detect unexpected leakage of a boiler tube in real time. This paper proposes an acoustic emission (AE)-based diagnostic system for detecting leakage of a circulating fluidized bed boiler tube using a data acquisition (DAQ) system and support vector machines (SVMs) for data acquisition, data analysis, and leakage detection. Experimental results show that the proposed diagnosis system perfectly detects leakage of a boiler tube on the simulation testbed.
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References
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Acknowledgment
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).
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Kim, YH., Jeong, IK., Kim, JY., Ban, JK., Kim, JM. (2020). Development of an Intelligent Diagnosis System for Detecting Leakage of Circulating Fluidized Bed Boiler Tubes. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_30
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DOI: https://doi.org/10.1007/978-3-030-14347-3_30
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