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Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.
Rocznik
Tom
Strony
351--356
Opis fizyczny
Bibliogr. 25 poz., wykr.
Twórcy
autor
- University of Chemical Technology and Metallurgy Sofia, Bulv. St. Kliment Ohridski 8, 1756 Sofia, Bulgaria
autor
- Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
autor
- Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
Bibliografia
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- [3] O. Dragomir, R. Gouriveau, F. Dragomir, E. Miuca, and N. Zerhoni, „Review of prognostic problem in condition-based maintenance”, Proc. of European Control Conference, pp. 25-30, 2009.
- [4] O. Dragomir, R. Gouriveau, and N. Zerhouni, „Adaptive neuro-fuzzy inference system for midterm prognostic error stabilization”, Inter. Jour. of Comp. Comm. and Control, vol. 3, pp. 271-276, 2008.
- [5] G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent fault II-ldynosis and prognosis for engineering systems. Wiley & Sons, 2006.
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- [8] P. Wang and G. Vachtsevanos, „Fault prognostics using dynamic wavelet neural networks”, Artificial Intelligence for Engineering Design Analysis and Manufacturing, vol. 15, pp. 349-265, 2001.
- [9] W. Q. Wang, M. F. Golnaraghi, and F. Ismail, „Prognosis of machine health condition using neuro-fuzzy systems”, Mechanical Systems and Signal Processing, vol. 18, pp. 813-831, 2004.
- [10] S. Zhang and R. Ganesan, „Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery”, Journal of Engineering for Gas Turbines and Power, vol. 119, pp. 378-384, 1997.
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- [13] R. Heoshmand and M. Banejad, „Application of fuzzy logic in fault diagnosis in transformers using dissolved gas based on different standards”, Word Academy of Sciences, Engineering and Technology, vol. 17, no. 2, pp. 157-161, 2006.
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- [15] B. Bonev, T. Totev, J. Artakov, and M. Nikolov, „Diagnostic of coal dust prepraration systems with milling fans”, in New Trends in Automation of Energetic Processes 98.
- [16] T. Totev, B. Bonev, and J. Artakov, „Systems for determination of the quality of coal, burning in the steamgenerator p-62 in tpp”, Maritza East Energetics, no. 1-2, 1995.
- [17] M. Hadjiski, M. Nikolov, S. Dukovski, G. Drianovski, and E. Tamnishki, „Low-rank coal fired boilers monitoring by applying hybrid models”, in 8th IEEE Mediterranean Conference on Control and Automation MED 2000, 2000.
- [18] M. Hadjiski and V. Totev, „Hybrid modeling of milling fan of steam generators in tpp”, Automatics and Informatics, no. 4, 2000.
- [19] M. Hadjiski, V. Totev, and R. Yusupov, „Softsensing-based flame position estimation in steam boiler combustion chamber”, in Distributed Computer and Communication Networks, 2005.
- [20] M. Hadjiski, V. Petkov, and E. Mihailov, „A software environment for approximate model design of a low-calorithic coal combustion in power plant boilers”, Modelling and Optimization of Pollutant Reduced Industrial Furnaces, 2000.
- [21] R. Eisenman, Machinery Malfunction Diagnosis and Correction. Diagnostyka 3 (39), 2006.
- [22] M. Tabaszewski, Forecasting of Residual Time of Milling Fans by Means of Neural Networks. Prentice-Hall, 1998.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAD-0032-0008