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Article:Artificial Neural Network Approach for Fault Detection in Pneumatic Valve in Cooler Water Spray System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 9 - Number 7 |
| Year of Publication: 2010 |
| Authors: P. Subbaraj, B. Kannapiran |
10.5120/1395-1881
|
P. Subbaraj, B. Kannapiran . Article:Artificial Neural Network Approach for Fault Detection in Pneumatic Valve in Cooler Water Spray System. International Journal of Computer Applications. 9, 7 ( November 2010), 43-52. DOI=10.5120/1395-1881
Abstract
The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. The early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. Since the operator cannot monitor all variables simultaneously, an automated approach is needed for the real time monitoring and diagnosis of the system. This paper presents the design and development of artificial neural network based model for the fault detection of Pneumatic valve in cooler water spray system in cement industry. The network is developed to detect a totally nineteen faults. The training and testing data required to develop the neural network model were generated at different operating conditions by operating the pneumatic valve and by creating various faults in real time in a laboratory experimental model. The performance of the developed back propagation is found to be satisfactory for the real time fault diagnosis.
References
- M.A. Sharif, R.I. Grosvenor, Sensor-based performance monitoring of a control valve unit, Proceedings of Institute of Mechanical Engineering—Journal of Process Mechanical Engineering 213 (E2) (1999) 71–84. 610 S.
- D. Renfrey, Total valve management—the key to better process plant operations, Sensor Review 15 (4) (1995) 25–27.
- J. McGhee, I.A. Henderson, A. Baird, Neural networks applied for the identification and fault diagnosis of process valves and actuators, Measurement 20 (4) (1997) 267–275.
- Patton, R. J., Frank, P. M., &Clark, R.N. Issues of fault diagnosis for dynamic systems. London: Springer (2000).
- S.W. Wang, J.B. Wang, Law-based sensor fault diagnosis and validation for building air-conditioning systems, International Journal of HVAC&R Research 5 (4) (1999) 353–378.
- A. Wallen, Valve diagnosis and automatic tuning, in: Proceedings of the American Control Conference, Albuquerque, New Mexico, USA, June 1997, pp. 2930–2934.
- A.L. Dexter, M. Benouarets, Generic approach to identifying faults in HVAC plants, ASHRAE Transactions: Symposia 102 (1) (1996) 550–556.
- T.I. Salsbury, R.C. Diamond, Fault detection in HVAC systems using model-based feedforward control, Energy and Buildings 33 (4) (2001) 403–415.
- Isermann.R, “Supervision, fault detection and fault diagnosis methods-an introduction”, International Journal of Control Engineering Practice, Vol.5, No.5, pp.639-652, 1997.
- Isermann.R and Balle, “Trends in the application of Model-based fault detection and diagnosis of technical process”, International Journal of Control Engineering Practice 5(5), pp 709-719, 1997.
- Leonhardt, S. and Ayoubi, M., “Methods of Fault diagnosis”, International Journal of Control Engineering Practice, 5(5), pp. 683-692, 1997.
- Venkat Venkatasubramanian, “A Review of Process Fault Detection and Diagnosis Part I, II & III. Computers and Chemical Engineering”, Elsevier Science Ltd. 2003.
- Lippmann.R.P., “Review of neural networks for speech recognition”, Neural Computation, Vol.1, pp.1-38, 1989.
- M. Karpenko, N. Sepehri, Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator, International Journal on Engineering Applications of Artificial Intelligence 15, (2002), 273-283
- G.E Hinton, D. E. Rumelhart, R. T. Williams, Learning Internal Representations by Error Propagation, vol. 1, MIT Press, Cambridge, MA, 1985 (reprinted in Anderson and Rosenfield, pp. 318-362).
- M. Karpenko, N. Sepehri, D. Scuse, Diagnosis of process valve actuator faults using a multilayer neural network, International Journal on Control Engineering Practice 11, (2003) 1289-1299.
- J. A. Leonard, A. K. Kramer, Diagnosing dynamic faults using modular neural nets, IEEE Expert (1993) 44-52.
- S. N. Sivanandham, S. Sumathi, S. N. Deepa, Introduction to Neural Networks using Matlab 6.0. Tata McGraw- Hill Publishing Company Limited. New Delhi, 2006.
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