Fault diagnosis on bottle filling plant using genetic-based neural network

Advances in Engineering Software

Volumn 42, Issue 12, 2011, Pages 1051-1058

  Demetgul, M ^a   Unal, M ^a   Tansel, I N ^b   Yaziciolu, O ^c  

^a MARMARA UNIVERSITY   (Turkey)

^b Florida International University   (United States)

^c Department of Industrial Engineering   (Turkey)

Author keywords

Back propagation algorithm; Bottle filling plant; Fault diagnosis; Genetic algorithm; Neural network; Pneumatic

Indexed keywords

ATMOSPHERIC PRESSURE; BACKPROPAGATION ALGORITHMS; BOTTLES; FAULT DETECTION; FILLING; GENETIC ALGORITHMS; GRAPHICAL USER INTERFACES; PNEUMATICS; PROBLEM SOLVING;

AIR PRESSURES; EXECUTION TIME; EXPERIMENTAL DATA; FAULT DIAGNOSTICS; FILLING PLANTS; FILLING SYSTEM; FITNESS FUNCTIONS; HIDDEN LAYERS; NEURAL NETWORKS ALGORITHMS; NORMAL OPERATING CONDITIONS; OPTIMAL CONFIGURATIONS; OUTPUT ERRORS; PNEUMATIC SYSTEM; SENSORY DATA; TRIAL-AND-ERROR PROCESS; WORK CELL;

NEURAL NETWORKS;

EID: 80052732209     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2011.07.004     Document Type: Article

References (40)

1. L.B. Jack, and A.K. Nandi Genetic algorithms for feature selection in machine condition monitoring with vibration signals IEE Proc Vision, Image, Signal Process 147 2000 205 212
2. S.C. Tan, C.P. Lim, and M.V.C. Rao A hybrid neural network model for rule generation and its application to process fault detection and diagnosis Eng Appl Art Intell 20 2007 203 213 (Pubitemid 44822659)
3. H. Hammouri, P. Kabore, S. Othman, and J. Biston Failure diagnosis and nonlinear observer. Application to a hydraulic process J Frank Inst 339 2002 455 478 (Pubitemid 35039772)
4. A. Saxena, and A. Saad Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems Appl Soft Comput 7 2007 441 454 (Pubitemid 44739445)
5. S.-H. Seong, S. Hur, J.-S. Kim, J.-T. Kim, W.-M. Park, and U.-C. Lee Development of diagnosis algorithm for the check valve with spectral estimations and neural network models using acoustic signals Annals Nucl Energy 32 2005 479 492 (Pubitemid 40200566)
6. B. Samanta Gear fault detection using artificial neural networks and support vector machines with genetic algorithms Mech Syst Signal Process 18 2004 625 644
7. B. Samanta Artificial neural networks and genetic algorithms for gear fault detection Mech Syst Signal Process 18 2004 1273 1282
8. L. Zhang, L.B. Jack, and A.K. Nandi Fault detection using genetic programming Mech Syst Signal Process 19 2005 271 289
9. Ş. Aykut, M. Demetgul, and I. Tansel Selection of optimum cutting condition of cobalt-based superalloy with GONNS The Int J Adv Manuf Technol 46 2010 957 967
10. I.N. Tansel, M. Demetgul, H. Okuyucu, and A. Yapici Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network The Int J Adv Manuf Technol 48 2010 95 101
11. Borairi M, Wang H. Actuator and sensor fault diagnosis of non-linear etie neural networks and adaptive parameter estimation technique. In: IEEE international conference on control applications. Trieste, Italy; 1998.
12. X.Z. Gao, and S.J. Ovaska Genetic algorithm training of elman neural network in motor fault detection Neural Comput Appl 11 2002 37 44 (Pubitemid 36156155)
13. Y.C. Huang Condition assessment of power transformers using genetic-based neural networks IEE Proc Sci Measur Technol 150 2003 19
14. G. Hong, L.B. Jack, and A.K. Nandi Feature generation using genetic programming with application to fault classification IEEE Trans Syst, Man, Cybern, Part B Cybern 35 2005 89 99 (Pubitemid 40226201)
15. Z. Yangping, Z. Bingquan, and W. DongXin Application of genetic algorithms to fault diagnosis in nuclear power plants Reliab Eng Syst Safety 67 2000 153 160 (Pubitemid 32212607)
16. F. Pettersson, N. Chakraborti, and H. Saxén A genetic algorithms based multi-objective neural net applied to noisy blast furnace data Appl Soft Comput 7 2007 387 397 (Pubitemid 44739441)
17. S. Yu, K. Zhu, and F. Diao A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction Appl Math Comput 195 2008 66 75 (Pubitemid 350213065)
18. W. Liu, Q. Liu, F. Ruan, Z. Liang, and H. Qiu Springback prediction for sheet metal forming based on GA-ANN technology J Mater Process Technol 187-188 2007 227 231 (Pubitemid 46412859)
19. M. Karpenko, and N. Sepehri Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator Eng Appl Art Intell 15 2002 273 283 (Pubitemid 35412474)
20. M. Karpenko, N. Sepehri, and D. Scuse Diagnosis of process valve actuator faults using a multilayer neural network Control Eng Pract 11 2003 1289 1299 (Pubitemid 37481821)
21. Nakutis Ž, Kaškonas P. Pneumatic cylinder diagnostics using classification methods. In: Instrumentation and measurement technology conference. Warsaw; 2007. p. 1-4.
22. Shi L, Sepehri N. Adaptive fuzzy-neural-based multiple models for fault diagnosis of a pneumatic actuator. In: American control conference. Boston; 2004.
23. M. Demetgul, I.N. Tansel, and S. Taskin Fault diagnosis of pneumatic systems with artificial neural network algorithms Expert Syst Appl 36 2009 10512 10519
24. S.L. Jämsä-Jounela, M. Vermasvuori, P. Endén, and S. Haavisto A process monitoring system based on the Kohonen self-organizing maps Control Eng Pract 11 2003 83 92 (Pubitemid 36225296)
25. B.S. Yang, T. Han, and J.L. An ART-KOHONEN neural network for fault diagnosis of rotating machinery Mech Syst Signal Process 18 2004 645 657
26. H.J. Shin, D.-H. Eom, and S.-S. Kim One-class support vector machines - an application in machine fault detection and classification Comput Indust Eng 48 2005 395 408 (Pubitemid 40274932)
27. Demetgul M. Fault diagnosis at pneumatic system with neural networks, PhD Thesis. stanbul: Marmara University. Institute of Graduate Studies in Pure and Applied Sciences; 2006.
28. M.T. Hagan, H.B. Demuth, and M. Beale Neural network design 1997 Thomson Boston
29. Ünal M, Erdal H, Topuz V. Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process. Comput Appl Eng Edu; 2010, doi: 10.1002/cae.20420.
30. J. Holland Adaptation in natural and artificial systems 1975 The University of Michigan Press
31. Ünal M. Optimization of PID controller using ant colony/genetic algorithms and control of the gunt RT 532 pressure process. Marmara University, stanbul, Turkey: Institute for Graduate Studies in Pure and Applied Sciences; 2008.
32. K.P. Ferentinos Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms Neural Networks 18 2005 934 950 (Pubitemid 41253509)
33. N. Jiang, Z. Zhao, and L. Ren Design of structural modular neural networks with genetic algorithm Adv Eng Softw 34 2003 17 24
34. F. Wang, V. Devabhaktuni, and Q.J. Zhang A hierarchical neural network approach to the development of library of neural models for microwave design Micro Theory Tech 46 1998 2391 2403 (Pubitemid 128738979)
35. H. Kitano Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms Physica D 75 1994 225 228
36. V. Maniezzo Genetic evolution of the topology and weight distribution of neural networks IEEE Trans Neural Networks 1994 5
37. A.J.F.v. Rooij, L.C. Jain, and R.P. Johnson Neural network training using genetic algorithms 1996 World Scientific
38. 30.12.2009. Advantech PCI 1711 DAQ Card.
39. 30.12.2009. MATLAB & SIMULINK.
40. Demuth H, Beale M, Hagan M. Neural network toolbox 6 user's guide. Mathworks; September 2009.