Robust Fault Detection on Boiler-turbine Unit Actuators Using Dynamic Neural Networks

Document Type : Research Article

Authors

1 َAmirkabir university

2 Editor-in-chief

3 Amirkabir University of Technology

4 Phd student at Tabriz university

Abstract

Due to the important role of the boiler-turbine units in industries and electricity generation, it is important to diagnose different types of faults in different parts of boiler-turbine system. Different parts of a boiler-turbine system like the sensor or actuator or plant can be affected by various types of faults. In this paper, the effects of the occurrence of faults on the actuators are investigated and analyzed and fault detection of boiler-turbine actuators is studied. For fault detection purpose, a dynamic neural network with an internal feedback is applied to generate the residual. After generating the residuals, the decision making step, as the most crucial part of the fault detection process, has to be followed. For designing a proper threshold, which is sensitive to different types of faults and insensitive to noise, the robust threshold is designed using the model error modeling method. The robust threshold is designed using a dynamic neural network with an internal feedback. The results for multiple types of faults and various outputs show the effectiveness of this approach for designing the threshold. As a practical case of study the dynamic model of the boiler-turbine unit, which was represented by Bell and Astrom in their paper, is considered.

Keywords

dor 20.1001.1.25882953.2019.51.2.1.2

Main Subjects


[1]           M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki, and J. Schrder, Diagnosis and fault-tolerant control: Springer Publishing Company, Incorporated, 2010.
[2]           M. Addel-Geliel, S. Zakzouk, and M. El Sengaby, "Application of model based fault detection for an industrial boiler," in Control & Automation (MED), 2012 20th Mediterranean Conference on, 2012, pp. 98-103.
[3]           G. Jianqiang, Y. Xianglei, and H. Zhifu, "Research on fuzzy recognition method of boiler four-tube leakage," in Intelligent Control and Information Processing (ICICIP), 2011 2nd International Conference on, 2011, pp. 391-393.
[4]           X. Min, W. Ying, and L. Yanjun, "Notice of Retraction Fault diagnosis of marine boiler superheater tube rupture based on fuzzy neural network," in Power Engineering and Automation Conference (PEAM), 2011 IEEE, 2011, pp. 389-392.
[5]           X. Sun, T. Chen, and H. J. Marquez, "Efficient model-based leak detection in boiler steam-water systems," Computers & chemical engineering, vol. 26, pp. 1643-1647, 2002.
[6]           W. Tan, F. Fang, L. Tian, C. Fu, and J. Liu, "Linear control of a boiler–turbine unit: Analysis and design," ISA transactions, vol. 47, pp. 189-197, 2008.
[7]           L. Khoshnevisan, H.-R. Momeni, and A. Ashraf-Modarres, "H∞ robust fault detection filter in drum boiler systems," in GCC Conference and Exhibition (GCC), 2011 IEEE, 2011, pp. 577-580.
[8]           A. Ghamari, H. Khaloozadeh, A. Ashraf-Modarres, and H. Ghamari, "Application of quantitative data-based fault detection methods on a drum-type boiler," in The 3rd Conference on Thermal Power Plants, 2011.
[9]           L. Khoshnevisan, S. Ozgoli, and M. H. Shojaei, "An ILMI approach to robust fault detection filter for a drum boiler system through a time-domain H-index norm method," in Electrical Engineering (ICEE), 2012 20th Iranian Conference on, 2012, pp. 922-927.
[10]         E. Rakhshani, I. Sariri, and K. Rouzbehi, "Application of data mining on fault detection and prediction in Boiler of power plant using artificial neural network," in Power Engineering, Energy and Electrical Drives, 2009. POWERENG'09. International Conference on, 2009, pp. 473-478.
[11]         A. Aitouche and B. Ould Bouamama, "Detecting and isolating actuators faults of steam boiler," in 8th International Conference on Sciences and Techniques of Automatic control, Sousse, Tunisia, 2007, pp. 5-7.
[12]         M. Berahman, A. Safavi, and M. R. Shahrbabaki, "Fault detection in Kerman combined cycle power plant boilers by means of support vector machine classifier algorithms and PCA," in Control, Instrumentation, and Automation (ICCIA), 2013 3rd International Conference on, 2013, pp. 290-295.
[13]         N. P. David and B. Swaminathan, "Modeling, identification and detection of faults in industrial boiler (July2015)," in Technological Innovation in ICT for Agriculture and Rural Development (TIAR), 2015 IEEE, 2015, pp. 197-201.
[14]         A. Daneshnia, M. B. Menhaj, F. Barazandeh, and A. Kazemi, "Robust fault detection on boiler-turbine unit actuators using dynamic neural networks," in 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA), 2016, pp. 251-255.
[15]         K. Astrom, "Simplified Models of Boiler—Turbine Units," 1987.
[16]         K. Astrom and K. Eklund, "A simple non-linear drum boiler model," International Journal of Control, vol. 22, pp. 739-740, 1975.
[17]         K. J. Åström and K. Eklund, "A simplified non-linear model of a drum boiler-turbine unit†," International Journal of Control, vol. 16, pp. 145-169, 1972.
[18]         R. Bell and K. J. Åström, Dynamic models for boiler-turbine-alternator units: data logs and parameter estimation for a 160 MW unit: Lund Institute of Technology, Department of Automatic Control, 1987.
[19]         H. Moradi, A. Alasty, M. Saffar-Avval, and F. Bakhtiari-Nejad, "Multivariable control of an industrial boiler-turbine unit with nonlinear model: A comparison between gain scheduling and feedback linearization approaches," Scientia Iranica. Transaction B, Mechanical Engineering, vol. 20, p. 1485, 2013.
[20]         W. Tan, Y. Niu, and J. Liu, "H∞ control for a boiler-turbine unit," in Control Applications, 1999. Proceedings of the 1999 IEEE International Conference on, 1999, pp. 910-914.
[21]         K. J. Åström and R. Bell, "Dynamic models for boiler-turbine alternator units: Data logs and parameter estimation for a 160 MW unit," Technical Reports, 1987.
[22]         K. Patan, Artificial neural networks for the modelling and fault diagnosis of technical processes vol. 377: Springer Science & Business Media, 2008.
[23]         H. A. Nozari, M. A. Shoorehdeli, and M. Mokhtareh, "Robust Fault Detection of an Industrial Gas Turbine Prototype: A Hybrid Passive Approach Based on Local Linear Neuro-Fuzzy Techniques."