Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF

Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF

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...

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Bibliographic Details
Main Author: M. I. Khalil
Format: Article
Language:English
Published: Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata 2010-10-01
Series:Journal of Computer Science and Technology
Subjects:
fault detection
neutron time-of- flight
neural networks
Online Access:https://journal.info.unlp.edu.ar/JCST/article/view/701
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spelling doaj-129b7236de054c548a5dd2c3f1c2c6792021-05-05T13:54:27ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382010-10-011003137142396Neural Network based Fault Diagnosis Procedure for the Detector System of CFDFM. I. Khalil0Reactor Physics Department, Nuclear Research Center, Atomic Energy Authority, Egypt.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.https://journal.info.unlp.edu.ar/JCST/article/view/701fault detectionneutron time-of- flightneural networks
collection DOAJ
language English
format Article
sources DOAJ
author M. I. Khalil
spellingShingle M. I. Khalil
Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
Journal of Computer Science and Technology
fault detection
neutron time-of- flight
neural networks
author_facet M. I. Khalil
author_sort M. I. Khalil
title Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_short Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_full Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_fullStr Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_full_unstemmed Neural Network based Fault Diagnosis Procedure for the Detector System of CFDF
title_sort neural network based fault diagnosis procedure for the detector system of cfdf
publisher Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
series Journal of Computer Science and Technology
issn 1666-6046
1666-6038
publishDate 2010-10-01
description 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.
topic fault detection
neutron time-of- flight
neural networks
url https://journal.info.unlp.edu.ar/JCST/article/view/701
work_keys_str_mv AT mikhalil neuralnetworkbasedfaultdiagnosisprocedureforthedetectorsystemofcfdf
_version_ 1721460457796009984

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