الفهرس 
REVIE-W OF IMPORTANT APPLICATIONS OF EXPERT SYSTEM FOR FAULT DIAGNOSISOnly 14 pages are availabe for public view
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Abstract
The Cairo Fourier Diffractometer Facility (CFDF) has been
installed at one of the ET-RR-l reactor horizontal channels and is based
on the reverse time of flight (RTOF) concept. The CFOF is a unique
neutron diffractometer and there is only four of such type around the
world. Unfortunately, the CFDF does not contain an integral control
panel to include all the functional indicators of the system. Moreover,
some devices of the system have no indicators at all. Since constructing
of a such control panel is out of the scope of this thesis; therefore it is
assumed that there is a central control panel which contains a selected
number of the most important indicators. The diagnosis of such type of
equipment is a very complex task. It depends on the conditions of the
indicators of the control panel of the device. The accurate diagnosis can
lead to a high system reliability and save much of the maintenance costs.
So an expert system for the CFDF fault diagnosis is needed to be built. In
this thesis, a hybrid expert system is developed for the fault diagnosis of
the CFDF. Two intelligent techniques, the multi layer feed forward-back
propagation neural network and the rule based expert system, are
integrated as a pre-processor loosely coupled model to build the proposed
hybrid expert system. The architecture of the developed hybrid expert
system consists of two levels. The first level is a feed forward back
propagation neural network, and is used for isolating the faulty part of the
CFDF. The second level is the rule based expert system, used for
troubleshooting the faults inside the isolated faulty part. The inputs of the
neural network level are the indicators conditions from the assumed
CFOF control panel (symptoms). The outputs correspond to the status of
the main parts of the CFDF. The back propagation training algorithm is
used for the neural networks training. Learning was performed using
different values of the learning factor 11,the momentum factor a and the
different number of hidden layers neurons are also investigated. The rule
based expert system takes the inputs and outputs of the neural networks
and also information from the user by questions and answers, to define
precisely the faults in the faulty part. The rule based expert system has
the features of being flexible enough to build or update the knowledge
base. The rule based expert system uses five independent knowledge
bases related to each of the CFDF main parts. It has been found that the
developed diagnostic system is both adequate and flexible for the CFDF .
. The present work is organized to fall in five chapters III
Chapter (1) presents the artificial intelligence strategies. The neural
networks, the fundamental components of the artificial neural network,
the artificial neural network learning, the activation functions and the
types of learning are investigated. Also a description for the rule based
expert system structure, the knowledge engineering, the rules as the
knowledge engineering, the rules as the knowledge representation scheme
for the knowledge base, the inference methods in forward and backward
chaining, the methods of fntegration of ;’heural networks and expert
systems, and the comparison between the neural networks and the expert
systems are included.
Chapter (2) reviews some of the previous works on the
applications of neural networks and expert systems for diagnosing
physical devices.
Chapter (3) presents a description of the CFDF parts, the symptoms
used for the fault diagnosis and its effect on the CFDF operation; and the •
conventional method for fault diagnosis and its disadvantage.
Chapter (4) deals with the design and implementation of the back
propagation algorithm and the experimental result for the network
training, the effect of changing the parameters learning factor, momentum
factor, hidden nodes number on the number of iterations used for
complete training. Also given are the steps for implementing the rule
based expert system.
Chapter (5) gives the conclusion and the future work for the
present work
The basic results of the present thesis were presented in a paper entitled:
Fault Diagnosis Using Artificial Neural Network
By: R. M. A. Maayouf, N. M. H. ElMinyawi, N. M. Ayad and
S. M. Abdelwahed.
The paper was published in : Arab J. of Nucl. Scien and Appl. 37(2),
May (2004), (233-244).
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