الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
Fault detection and diagnosis (FDD) have become critical issues, particularly for
industrial systems that rely on sophisticated control capabilities. FDD ensures
project performance requirements such as reliability, survivability,
maintainability, availability, cost efficiency, safety, and quality. This thesis is
concerned with FDD monitoring framework execution, detecting system faults,
isolating the broken components (faults) within the system, and then identifying
faults. Two different AI methodologies were proposed and applied in two
different case studies for FDD. Those methodologies are FDD based on Recurrent
Neural Networks (RNNs) and Distributed Neural Networks (DNNs). Overall,
both methodologies demonstrated the potential of neural networks, specifically
RNNs and DNNs, in fault detection and diagnosis in dynamic systems. They show
the importance of preprocessing data and training neural networks with labelled
data for accurate fault detection. These methodologies could be applied to
enhance system reliability and performance in various engineering systems such
as level control systems and network of distributed motors.