Leakage Detection of Steam Boiler Tube in Thermal Power Plant Using Principal Component Analysis

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Jungwon Yu Jaeyel Jang Jaeyeong Yoo June Ho Park Sungshin Kim

Abstract

Tube leakage of steam boiler can decrease the whole efficiency of power plant cycle, and eventually cause an unscheduled shutdown. In this paper, we propose a leakage detection method for steam boiler tubes in thermal power plant (TPP) using principal component analysis and exponentially weighted moving average (EWMA). To determine the number of principal components, the cumulative percent variance technique is employed, and the Q statistic is used as the detection index. If the Q statistic of an unseen sample is larger than a predefined threshold value, the sample is detected as a fault sample and an alarm signal is generated. EWMA is used to reduce false alarms. To demonstrate the performance, we apply the proposed method to an unplanned shutdown case due to boiler tube leakage, which is collected from distributed control systems of 250 MW coal-fired TPP. The experiment results show that the proposed method can detect failure symptoms of the case successfully.

How to Cite

Yu, J., Jang, J., Yoo, J., Park, J. H., & Kim, S. (2016). Leakage Detection of Steam Boiler Tube in Thermal Power Plant Using Principal Component Analysis. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2510
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Keywords

PHM

References
Afgan, N., Coelho, P. J., & Carvalho, M. G. (1998). Boiler tube leakage detection expert system. Applied Thermal Engineering, vol. 18, no. 5, pp. 317-326. doi: 10.1016/S1359-4311(97)00054-9
Ajami, A., & Daneshvar, M. (2012). Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA). International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 728-735. doi: 10.1016/j.ijepes.2012.06.022
An, L., Wang, P., Sarti, A., Antonacci, F., & Shi, J. (2011). Hyperbolic boiler tube leak location based on quaternary acoustic array. Applied Thermal Engineering, vol. 31, no. 16, pp. 3428-3436. doi: 10.1016/j.applthermaleng.2011.06.028
Beghi, A., Brignoli, R., Cecchinato, L., Menegazzo, G., Rampazzo, M., & Simmini, F. (2016). Data-driven fault detection and diagnosis for HVAC water chillers. Control Engineering Practice, vol. 53, 79-91. doi: 10.1016/j.conengprac.2016.04.018
Flynn, D. (2003). Thermal power plant simulation and control (No. 43). IET.
Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. Elsevier.
Harrou, F., Nounou, M., & Nounou, H. (2013) A statistical fault detection strategy using PCA based EWMA control schemes, Control Conference (ASCC), 2013 9th Asian, Istanbul, 2013, pp. 1-4. doi: 10.1109/ASCC.2013.6606311
Harrou, F., Nounou, M. N., Nounou, H. N., & Madakyaru, M. (2013). Statistical fault detection using PCA-based GLR hypothesis testing. Journal of Loss Prevention in the Process Industries, vol. 26, no. 1, pp. 129-139. doi: 10.1016/j.jlp.2012.10.003
Harrou, F., Kadri, F., Chaabane, S., Tahon, C., & Sun, Y. (2015). Improved principal component analysis for anomaly detection: Application to an emergency department. Computers & Industrial Engineering, vol. 88, pp. 63-77. doi: 10.1016/j.cie.2015.06.020
Jackson, J. E., & Mudholkar, G. S. (1979). Control procedures for residuals associated with principal component analysis. Technometrics, vol. 21, no. 3, pp. 341-349. doi: 10.1080/00401706.1979.10489779
Joe Qin, S. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics, vol. 17, no. 8‐9, pp. 480-502. doi: 10.1002/cem.800
Oakey, J. E. (Ed.). (2011). Power plant life management and performance improvement. Elsevier.
Patan, K. (2008). Artificial neural networks for the modelling and fault diagnosis of technical processes. Springer. doi: 10.1007/978-3-540-79872-9
Peng, X., Li, Q., & Wang, K. (2015). Fault detection and isolation for self powered neutron detectors based on Principal Component Analysis. Annals of Nuclear Energy, vol. 85, pp. 213-219. doi: 10.1016/j.anucene.2015.05.016
Raja, A. K. (2006). Power plant engineering. New Age International.
Rostek, K., Morytko, Ł., & Jankowska, A. (2015). Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks. Energy, vol. 89, pp. 914-923. doi: 10.1016/j.energy.2015.06.042
Sarkar, D. (2015). Thermal power plant: design and operation. Elsevier.
Sun, X., Chen, T., & Marquez, H. J. (2002). Efficient model-based leak detection in boiler steam-water systems. Computers & Chemical Engineering, vol. 26, no. 11, pp. 1643-1647. doi: 10.1016/S0098-1354(02)00147-3
Sun, X., Marquez, H. J., Chen, T., & Riaz, M. (2005). An improved PCA method with application to boiler leak detection. ISA transactions, vol. 44, no. 3, pp. 379-397. doi: 10.1016/S0019-0578(07)60211-0
Wang, X., Ma, L., & Wang, T. (2014). An optimized nearest prototype classifier for power plant fault diagnosis using hybrid particle swarm optimization algorithm. International Journal of Electrical Power & Energy Systems, vol. 58, pp. 257-265. doi: 10.1016/j.ijepes.2014.01.016
Widarsson, B., & Dotzauer, E. (2008). Bayesian network-based early-warning for leakage in recovery boilers. Applied Thermal Engineering, vol. 28, no. 7, pp. 754-760. doi: 10.1016/j.applthermaleng.2007.06.016
Yu, J., Jang, J., Yoo, J., Park, J. H., & Kim, S. A Clustering-Based Fault Detection Method for Steam Boiler Tube in Thermal Power Plant. Journal of Electrical Engineering & Technology, to be published.
Zhang, S., Shen, G., An, L., & Gao, X. (2015). Power station boiler furnace water-cooling wall tube leak locating method based on acoustic theory. Applied Thermal Engineering, vol. 77, pp. 12-19. doi: 10.1016/j.applthermaleng.2014.12.015
Zhao, K., & Upadhyaya, B. R. (2006). Model based approach for fault detection and isolation of helical coil steam generator systems using principal component analysis. Nuclear Science, IEEE Transactions on, vol. 53, no. 4, pp. 2343-2352. doi: 10.1109/TNS.2006.876049
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Technical Papers