Fault diagnosis of pneumatic systems with artificial neural network algorithms

Expert Systems with Applications

Volumn 36, Issue 7, 2009, Pages 10512-10519

  Demetgul, M ^a   Tansel, I N ^b   Taskin, S ^c  

^a MARMARA UNIVERSITY   (Turkey)

^b Florida International University   (United States)

^c CELAL BAYAR UNIVERSITY   (Turkey)

Author keywords

Adaptive resonance theory2 (ART2); Back propagation (Bp); Fault diagnosis; Modular production system; Pneumatic

Indexed keywords

ADAPTIVE RESONANCE THEORIES; ADAPTIVE RESONANCE THEORY2 (ART2); ARTIFICIAL NEURAL NETWORK ALGORITHMS; ARTIFICIAL NEURAL NETWORKS; BACK PROPAGATION (BP); ENCODED SIGNALS; ESTIMATION ERRORS; FAULT DIAGNOSIS; FAULTY OPERATIONS; LOW AIR PRESSURES; MODULAR PRODUCTION SYSTEM; NORMAL MODES; OUTPUT RANGES; PNEUMATIC SYSTEMS; PRESSURE VALVES; SEQUENTIAL DATUM; SYNTHETIC DATUM; TEST DATUM;

ATMOSPHERIC PRESSURE; BACKPROPAGATION; ELECTRIC FAULT CURRENTS; PNEUMATIC CONTROL; PNEUMATIC EQUIPMENT; PRODUCTION ENGINEERING; RESONANCE; SCADA SYSTEMS;

NEURAL NETWORKS;

EID: 67349214658     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.01.028     Document Type: Article

References (27)

1. Angeli C., and Smirni N. An online expert system for fault diagnosis in hydraulic systems. Expert System May (1999) 115-120
2. Bayi{dotless}r, R., & Bay, Ö. F. (2004). Serial wound starter motor faults diagnosis using artificial neural network. In IEEE ICM'04, İstanbul. pp. 194-199.
3. Carpenter G.A., and Grossberg S. ART 2: Self organization of stable category recognition codes for analog input patterns. Applied Optics 26 23 (1987) 4919-4930
4. Chen D., and Wang J.W. Classification of wavelet map patterns using multi-layer neural networks for gear fault detection. Mechanical Systems and Signal Processing 16 (2000) 695-704
5. Demetgul M. (2006). Fault diagnosis on pneumatic system with neural networks, Marmara University Institute of Science & Technology, Ph.D. Thesis.
6. Demetgul M. Experiment based fault diagnosis on bottle filling plant with LVQ artificial neural network algorithm. Pamukkale University Journal of Engineering Science 14 (2008) 92-100
7. Hussain, T., & Frey, G. (2005). Migration of a PLC controller to an IEC 61499 compliant distributed control system: Hands-on experiences. In Proceedings of the 2005 IEEE international conference on robotics and automation Barcelona, Spain.
8. Jams a-Jounela S.-L., Vermasvuori M., Enden P., and Haavisto S. A process monitoring system based on the Kohonen self-organizing maps. Control Engineering Practice 11 (2003) 83-92
9. Karkoub M.A., Gad O.E., and Rabie M.G. Predicting axial piston pump performance using neural network. Mechanism and Machine Theory 34 (1999) 1211-1226
10. Karpenko M., and Sepehri N. Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator. Engineering Applications of Artificial Intelligence 15 3-4 (2002) 273-283
11. Karpenko M., Sepehri N., and Scuse D. Diagnosis of process valve actuator faults using a multilayer neural network. Control Engineering Practice 11 11 (2003) 1289-1299
12. Masters T. Advanced algorithms for neural networks (1995), John Wiley & Sons Inc, New York
13. McGhee J., Henderson I.A., and Baird A. Neural network applied for the identification and fault diagnosis of process valves and actuators. Measurement 20 (1997) 267-275
14. Parlos A.G., Kim K., and Bharadwaj R.M. Sensorless detection of mechanical faults in electromechanical systems. Mechatronics 14 (2004) 357-380
15. Rajakarunakaran S., Venkumar P., Devaraj D., and Rao K.S.P. Artificial neural network approach for fault detection in rotary system. Applied Soft Computing 8 (2008) 740-748
16. Rumelhart D.E., Hilton G., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart E., and McClelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition Vol. 1 (1986), MIT Press
17. Samanta B. Gear fault detection using artificial neural network and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing 12 (2004) 625-644
18. Sandt, J., Rinkinen, J.,& Laukka, J. (1997). Particle and water online monitoring for hydraulic system diagnosis. In The fifth Scandinavian international conference on fluid power, (Vol. 3, pp. 257-268). Linköping: Sweden.
19. Seong S.H., Hur S., Kim J.S., Kim J.T., Park W.M., Lee U.C., et al. Development of diagnosis algorithm for the check valve with spectral estimations and neural network models using acoustic signals. Annals of Nuclear Energy 32 (2005) 479-492
20. Shi, L., & Sepehri, N. (2005). Fault diagnosis of pneumatic actuator using adaptive network-based fuzzy interference system models and a learning vector quantization neural network. In IEEE Proceeding of the fourth international conference on machine learning and cybernetics, Guangzhou. pp. 18-31.
21. Tansel I.N., Wagiman A., and Tziranis A. Recognition of chatter with neural networks. International Journal of Machine Tools and Manufacture 31 4 (1991) 539-552
22. Tansel I.N., Mekdeci C., and McLaughlin C. Dissection of tool failure in end milling with wavelet transformations and neural networks. International of Journal Machine Tools Manufacturing 35 8 (1995) 1137-1147
23. Taskin, S. (2007). Computer aided real time control of mps modular production system and technical education application, Marmara University Institute of Science & Technology, Ph.D. Thesis.
24. Venkatasubramanian V., Rengaswamy R., Kavuri S.N., and Yin K. A review of process fault detection and diagnosis part III: Process history based methods. Computers and Chemical Engineering 27 (2003) 327-346
25. Wang W.Q., Golnaraghi M.F., and Ismail F. Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing 18 (2004) 813-831
26. Wuxing L., Tse P.W., Guicai Z., and Tielin S. Classification of gear faults using cumulants and the radial basis function network. Mechanical Systems and Signal Processing 18 (2004) 381-389
27. Yang B.S., Han T., and An J.L. ART-Kohonen neural network for fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing 18 13 (2004) 645-657