Feature Selection of Multi-Channel Acoustic Signals Based on Weighted KL Distance for Boiler Tube Leakage Detection
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OpenAlex topics: Water Systems and Optimization · Fire Detection and Safety Systems · Flow Measurement and Analysis
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References (33)
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1. Khalid, S., Lim, W., Kim, H.S., et al.: Intelligent steam power plant boiler waterwall tube leakage detection via machine learning-based optimal sensor selection. Sensors 20(21), 6356 (2020). https://doi.org/10.3390/s2021635610.3390/s20216356 ↗
2. Wang, C., Wang, Q., Wang, Y., et al.: Pci reverberation suppression method for boiler tube leakage based on acoustic signal correlation. IEEE Trans. Instrum. Meas. (2025). https://doi.org/10.1109/TIM.2025.354472910.1109/TIM.2025.3544729 ↗
3. Barma, M.C., Saidur, R., Rahman, S.M.A., et al.: A review on boilers energy use, energy savings, and emissions reductions. Renew. Sustain. Energy Rev. 79, 970–983 (2017). https://doi.org/10.1016/j.rser.2017.05.18710.1016/j.rser.2017.05.187 ↗
4. Sun, W.J., Zhao, Y.L., Chen, Z.Z.: A new detection method of boiler tubes leakage based on information fusion technology. Shaanxi Electr Power 41(9), 77–81 (2013)
5. Studdard, B.M., Arrington, P., Rechner, M.R.: Early boiler leak detection cuts costs; detection system helps to pinpoint boiler leaks fast at Alabama Power’s Gaston Plant. Power Eng. 97(9), 25–28 (1993)
6. Panday, R., Indrawan, N., Shadle, L.J., et al.: Leak detection in a subcritical boiler. Appl. Therm. Eng. 185, 116371 (2021). https://doi.org/10.1016/j.applthermaleng.2020.11637110.1016/j.applthermaleng.2020.116371 ↗
7. Duong, B.P., Kim, J., Kim, C.H., et al.: Deep learning object-impulse detection for enhancing leakage detection of a boiler tube using acoustic emission signal. Appl. Sci. 9(20), 4368 (2019). https://doi.org/10.3390/app920436810.3390/app9204368 ↗
8. Sohaib, M., Kim, J.M.: Data driven leakage detection and classification of a boiler tube. Appl. Sci. 9(12), 2450 (2019). https://doi.org/10.3390/app912245010.3390/app9122450 ↗
9. Kim, Y.H., Kim, J., Kim, J.M.: Leakage detection of a boiler tube using a genetic algorithm-like method and support vector machines. In: Proc 10th Int Conf Soft Comput Pattern Recogn (SoCPaR), pp 86–93. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-17065-3_910.1007/978-3-030-17065-3_9 ↗
10. Xiao, R., Hu, Q., Li, J.: Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine. Measurement 146, 479–489 (2019). https://doi.org/10.1016/j.measurement.2019.06.0510.1016/j.measurement.2019.06.05 ↗
11. Li, S., Song, Y., Zhou, G.: Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition. Measurement 115, 39–44 (2018). https://doi.org/10.1016/j.measurement.2017.10.0210.1016/j.measurement.2017.10.02 ↗
12. Tu, L.T.N., Kim, J.M.: Discriminative feature analysis based on the crossing level for leakage classification in water pipelines. J. Acoust. Soc. Am. 145(6), 611–617 (2019). https://doi.org/10.1121/1.511380910.1121/1.5113809 ↗
13. An, L.S., Tong, P., Jiang, G.S.: Research on weak leak detection of boiler tubes under strong background noise. Adv. Mater. Res. 287, 2481–2484 (2011). https://doi.org/10.4028/www.scientific.net/AMR.287-290.248110.4028/www.scientific.net/AMR.287-290.2481 ↗
14. Ramezani, M.G., Hasanian, M., Golchinfar, B. et al.: Automatic boiler tube leak detection with deep bidirectional LSTM neural networks of acoustic emission signals. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020. SPIE, 11379, pp 205–213. (2020). https://doi.org/10.1117/12.255888510.1117/12.2558885 ↗
15. Wang, C., Liu, H., Wang, Q.: Time delay estimation of furnace tube leakage acoustic signal based on generalized cross correlation. In: Proc 42nd Chin Control Conf (CCC), pp 3460–3464. (2023). https://doi.org/10.23919/CCC58697.2023.1024061210.23919/CCC58697.2023.10240612 ↗
16. Bortel, R., Sovka, P.: Approximation of statistical distribution of magnitude squared coherence estimated with segment overlapping. Signal Process. 87(5), 1100–1117 (2007). https://doi.org/10.1016/j.sigpro.2006.10.00310.1016/j.sigpro.2006.10.003 ↗
17. Gallet, C., Julien, C.: The significance threshold for coherence when using the Welch’s periodogram method: effect of overlapping segments. Biomed. Signal Process. Control 6(4), 405–409 (2011). https://doi.org/10.1016/j.bspc.2010.11.00410.1016/j.bspc.2010.11.004 ↗
18. An, L., Shen, G., Zhang, B., et al.: Experiment study on acoustic pyrometry in power plant boiler. Power Syst. Eng. 23(2), 23–25 (2007)
19. Zhang, S.P., An, L.S., Shen, G.Q., et al.: Study on characteristic of sound field and hot-state background acoustic noise in boiler furnace of acoustic pyrometry. J. North China Electr. Power Univ. 38(6), 68–74 (2011)
20. Ma, D.Y.: Foundations of modern acoustics theory. Science Press, Beijing (2004)
21. Du, G.H., Zhu, Z.M. et al.: Acoustics fundamentals. Nanjing Univ Press, Nanjing (2001)
22. Ma, D.Y.: Development of the turbulent jet noise law. Acta Acust. 12(5), 321–328 (1987)
23. Ahn, B., Kim, J., Choi, B.: Artificial intelligence-based machine learning considering flow and temperature of the pipeline for leak early detection using acoustic emission. Eng. Fract. Mech. 210, 381–392 (2019). https://doi.org/10.1016/j.engfracmech.2018.03.0110.1016/j.engfracmech.2018.03.01 ↗
24. Ning, F., Cheng, Z., Meng, D., et al.: A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification. Appl. Acoust. 182, 108255 (2021). https://doi.org/10.1016/j.apacoust.2021.10825510.1016/j.apacoust.2021.108255 ↗
25. Wang, A., Hao, Y., Xing, Z., et al.: Continuous small leakage identification method of urban pipeline based on improved MVMD fusion machine learning. J. Nondestruct. Eval. 44(4), 135 (2025). https://doi.org/10.1007/s10921-025-01275-w10.1007/s10921-025-01275-w ↗
26. Song, Y., Li, S.: Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network. Process. Saf. Environ. Prot. 146, 736–744 (2021). https://doi.org/10.1016/j.psep.2020.11.05310.1016/j.psep.2020.11.053 ↗
27. Xiao, R., Hu, Q., Li, J.: A model-based health indicator for leak detection in gas pipeline systems. Measurement 171, 108843 (2021). https://doi.org/10.1016/j.measurement.2020.108810.1016/j.measurement.2020.1088 ↗
28. Ming, P., Lu, J., Cai, X., et al.: Experimental study on monitoring of dike piping process based on acoustic emission technology. J. Nondestruct. Eval. 40(1), 23 (2021). https://doi.org/10.1007/s10921-021-00752-210.1007/s10921-021-00752-2 ↗
29. Huang, H.B., Li, R.X., Huang, X.R., et al.: Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine. Appl. Acoust. 113, 137–148 (2016). https://doi.org/10.1016/j.apacoust.2016.06.01610.1016/j.apacoust.2016.06.016 ↗
30. Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: A review. In: Aggarwal CC (ed) Data Classification: Algorithms and Applications. CRC Press, Boca Raton, pp 37–64. (2014). https://doi.org/10.1201/b1732010.1201/b17320 ↗
31. Xu, T., Zeng, Z., Huang, X., et al.: Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector. Process. Saf. Environ. Prot. 153, 167–177 (2021). https://doi.org/10.1016/j.psep.2021.07.02410.1016/j.psep.2021.07.024 ↗
32. Felix, L.O., de Sá Só Martins, D.H.C., Monteiro, U.A.B.V., et al.: A feature selection committee method using empirical mode decomposition for multiple fault classification in a wind turbine gearbox. J. Nondestruct. Eval. 42(4), 85 (2023). https://doi.org/10.1007/s10921-023-00996-010.1007/s10921-023-00996-0 ↗
33. Urbanowicz, R.J., Meeker, M., La Cava, W., et al.: Relief-based feature selection: introduction and review. J. Biomed. Inform. 85, 189–203 (2018). https://doi.org/10.1016/j.jbi.2018.07.01410.1016/j.jbi.2018.07.014 ↗