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Journal of Engineering and Applied Sciences
Year: 2017 | Volume: 12 | Issue: 6 SI | Page No.: 7889-7896
DOI: 10.36478/jeasci.2017.7889.7896  
Neuro Fuzzy Model For Equipment Health Management in Yellow Phosphorus Production Process
Batyrbek Suleimenov, Laura Sugurova, Aituar Suleimenov and Alibek Suleimenov
 
References
Bilski, P., 2014. Data set preprocessing methods for the artificial intelligence-based diagnostic module. Meas., 54: 180-190.
CrossRef  |  Direct Link  |  

Birger, I.A., 1978. Technical Diagnostics. Mashinostroenie Publishers, Moscow, Russia,.

Borisov, V.V., V.V. Kruglov and A.S. Fedulov, 2007. Fuzzy models and networks. Hotline City, Moscow, Russia.

Boseniuk, T., V.D. Meer and M.T. Poschel, 1990. A multiprocessor system for high speed simulation of neural networks. J. New Gener. Comput. Syst., 3: 65-71.

Fedin, S.S. and R.M. Trisch, 2006. Quality assurance of complex technical objects through neural network diagnostics. East. Eur. J. Adv. Technol., 2: 78-82.

Hao, X. and S. Cai-Xin, 2007. Artificial immune network classification algorithm for fault diagnosis of power transformer. IEEE. Trans. Power Delivery, 22: 930-935.
CrossRef  |  Direct Link  |  

Huang, Y.C., 2003. Evolving neural nets for fault diagnosis of power transformers. IEEE. Trans. Power Delivery, 18: 843-848.
CrossRef  |  Direct Link  |  

Ionov, M.V. and M.N. Krasnyansky, 2012. Automated technical diagnostics systems of chemical engineering equipment. Prob. Mod. Sci. Pract., 2: 66-73.

Jaber, M., J. Combaz, L. Strus and J.C. Fernandez, 2008. Using neural networks for quality management. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation ETFA, September 15-18, 2008, IEEE, Hamburg, Germany, ISBN:978-1-4244-1505-2, pp: 1441-1448.

Jang, J.S., 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE. Trans. Syst. Man Cybern., 23: 665-685.
CrossRef  |  Direct Link  |  

Konstantinov, I.S., K.O. Polshchykov and S.A. Lazarev, 2016. Algorithm for neuro-fuzzy control of data sending intensity in a mobile ad hoc network for special purpose. J. Curr. Res. Sci., 4: 105-108.
Direct Link  |  

Kosko, В., 1991. Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall, Upper Saddle River, New Jersey, USA., ISBN:9780136114352, Pages: 449.

Miranda, V. and A.R.G. Castro, 2005. Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks. IEEE. Trans. Power Delivery, 20: 2509-2516.
CrossRef  |  Direct Link  |  

Naresh, R., V. Sharma and M. Vashisth, 2008. An integrated neural fuzzy approach for fault diagnosis of transformers. IEEE. Trans. Power Delivery, 23: 2017-2024.
CrossRef  |  Direct Link  |  

Nauck, D., F. Klawonn and R. Kruse, 1997. Foundations of Neuro-Fuzzy Systems. John Wiley & Sons, Hoboken, New Jersey, ISBN:9780471971511, Pages: 316.

Nguyen, D.T., Q.B. Duong, E. Zamai and M.K. Shahzad, 2016. Fault diagnosis for the complex manufacturing system. Proc. Inst. Mech. Eng. Part O. J. Risk Reliab., 230: 178-194.
Direct Link  |  

Parkhomenko, P.P., 2009. Organization of self-diagnosis of the discrete multicomponent systems a structure like bipartite quasicomplete graphs. Autom. Remote Control, 70: 907-915.
CrossRef  |  Direct Link  |  

Pegat, A., 2009. [Fuzzy Modeling and Control]. BINOM Publisher, BINOM Labo-ratoriya Znanii, Moscow, Russia, (In Russian).

Rutkovska, D., M. Pilinsky and L. Rutkovski, 2004. Neural networks: Genetic algorithms and fuzzy systems. Professional Women’s Network Warsaw, Poland.

Subbotin, S. and A. Oleynik, 2008. The multi objective evolutionary feature selection. Proceedings of the International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science, February 19-23, 2008, IEEE, Lviv-Slavsko, Ukraine, ISBN:978-966-553-678-9, pp: 115-116.

Suleimenov, B.A., M.S. Zhunisbekov, L.A. Sugurova and A.B. Suleimenov, 2014. Intelligent and hybrid process control systems: Theory, methods and applications. KSTU. Sci. Bull., 3: 98-106.

Suleimenov, B.A., M.S. Zhunisbekov, L.A. Sugurova and A.B. Suleimenov, 2014. The methodology of runtime diagnostics system of the production equipment condition. KSTU. Sci. Bull., 3: 107-118.

Swedrowski, L., K. Duzinkiewicz, M. Grochowski and T. Rutkowski, 2014. Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor. In: Key Engineering Materials, Tadeusz, U. (Ed.). Scientific.Net, Zurich, Switzerland, pp: 333-342.

Uvaysov, S., I. Ivanov, A. Tikhonov and A. Abrameshin, 2014. Definition of a Set of Diagnostic Features at a Given Depth and Completeness of Testing Electronic. In: Daaam International Scientific Book 2014, Katalinic, B. (Ed.). DAAAM International Publishing, Vienna, Austria, ISBN:978-3-901509-98-8, pp: 625-632.

You, Z.P., X.P. Ye and W.H. Zhang, 2014. Hydraulic system fault diagnosis method based on HPSO and WP-EE. Applied Mech. Mater., 577: 438-442.
CrossRef  |  Direct Link  |  

Yousif Yahya, A.Q. and A. Yahya, 2016. Power transformer fault diagnosis using fuzzy reasoning spiking neural systems. J. Intell. Learn. Syst. Appl., 8: 77-91.
Direct Link  |