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Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
Keywords:
Fault Detection, Neutron Time-of- Flight, Neural NetworksAbstract
This paper outlines and deals with the problem of fault detection, isolation and identification of the four-elements detector system attached to the Cairo Fourier diffractometer facility (CFDF) used for neutron time-of-flight (TOF) spectrum measurements. A feed forward neural network and error back propagation training algorithm are employed to diagnose four commonly occurring faults of the detector system: preamplifier, amplifier, discriminator and the high voltage. The diagnostic system processes the acquired data to determine whether the detector system state is normal or not. The experimental results showed that the trained network has the capability to detect and identify various faults which can make one of the detector units to be out of order.
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References
[1] H. Poyry, P. Hiismiki and A. Virjo, Nucl. Instr. and Meth.; 126, 421 (1975).
[2] H. Poyry, Nucl. Instr. and Meth.; 156, 499 (1978).
[3] P. Hiismaki, J. Junttila and A. Piirto, Nucl. Instr. and Meth.; 126, 435 (1975).
[4] J. Schroder, V.A. Kudryashev, J.M. Keuter, H.G. Priesmeyer, J. Larsen, and A. Tiitta, J. Neutron Res.; 2(4),129 (1994).
[5] V.L. Aksenov, A.M. Balagurov, G.D. Bokuchava, V.G. Simkin, V.A. Trounov , P. Hiismaki, J. Neutron Res.; 181 (1997).
[6] F. Gompf, IAEA Symp. Neutron Inelastic Scattering, Copenhagen, SH-104/77; 417 (1968).
[7] J.F. Colwell, P.M. Miller, W.L. Whittemore, IAEA Symp. Neutron Inelastic Scattering, Copenhagen, SM-104/77; 429 (1968).
[8] R.M.A. Maayouf, IAEA-SR-198/15, International. Seminar on Enhancement of Research Reactor Utilization, Atomic Research Center, Bombay, India, 11-15 March (1996).
[9] R.M.A. Maayouf and M.I. Khalil; Nucl. Instr. and Meth.; 484, 459 (2002).
[10] M. I. Khalil; Meas. Sci. Technol.; 16, 1-6 (2005).
[11] Liu, Jinhui and Gu, Fangyu; Nuclear Technology; 140 (2002).
[12] D. Liu and Z. Lu; IEEE Transactions on Neural Networks; 8(6), 1468 (1997).
[13] A. Abraham, "Artificial Neural Networks, Handbook for Measurement Systems Design, Sydenham and Richard Thorn" (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, 901 (2005).
[2] H. Poyry, Nucl. Instr. and Meth.; 156, 499 (1978).
[3] P. Hiismaki, J. Junttila and A. Piirto, Nucl. Instr. and Meth.; 126, 435 (1975).
[4] J. Schroder, V.A. Kudryashev, J.M. Keuter, H.G. Priesmeyer, J. Larsen, and A. Tiitta, J. Neutron Res.; 2(4),129 (1994).
[5] V.L. Aksenov, A.M. Balagurov, G.D. Bokuchava, V.G. Simkin, V.A. Trounov , P. Hiismaki, J. Neutron Res.; 181 (1997).
[6] F. Gompf, IAEA Symp. Neutron Inelastic Scattering, Copenhagen, SH-104/77; 417 (1968).
[7] J.F. Colwell, P.M. Miller, W.L. Whittemore, IAEA Symp. Neutron Inelastic Scattering, Copenhagen, SM-104/77; 429 (1968).
[8] R.M.A. Maayouf, IAEA-SR-198/15, International. Seminar on Enhancement of Research Reactor Utilization, Atomic Research Center, Bombay, India, 11-15 March (1996).
[9] R.M.A. Maayouf and M.I. Khalil; Nucl. Instr. and Meth.; 484, 459 (2002).
[10] M. I. Khalil; Meas. Sci. Technol.; 16, 1-6 (2005).
[11] Liu, Jinhui and Gu, Fangyu; Nuclear Technology; 140 (2002).
[12] D. Liu and Z. Lu; IEEE Transactions on Neural Networks; 8(6), 1468 (1997).
[13] A. Abraham, "Artificial Neural Networks, Handbook for Measurement Systems Design, Sydenham and Richard Thorn" (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, 901 (2005).
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Published
2010-10-01
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[1]
“Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF”, JCS&T, vol. 10, no. 03, pp. p. 137–142, Oct. 2010, Accessed: Jan. 14, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/701
