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Distributed fault detection and diagnostics using artificial intelligence techniques / A. Lucouw

With the advancement of automated control systems in the past few years, the focus
has also been moved to safer, more reliable systems with less harmful effects on the
environment. With increased job mobility, less experienced operators could cause more
damage by incorrect identification and handling of plant faults, often causing faults to
progress to failures. The development of an automated fault detection and diagnostic
system can reduce the number of failures by assisting the operator in making correct
decisions. By providing information such as fault type, fault severity, fault location
and cause of the fault, it is possible to do scheduled maintenance of small faults rather
than unscheduled maintenance of large faults.
Different fault detection and diagnostic systems have been researched and the best
system chosen for implementation as a distributed fault detection and diagnostic
architecture. The aim of the research is to develop a distributed fault detection and
diagnostic system. Smaller building blocks are used instead of a single system that
attempts to detect and diagnose all the faults in the plant.
The phases that the research follows includes an in-depth literature study followed by
the creation of a simplified fault detection and diagnostic system. When all the aspects
concerning the simple model are identified and addressed, an advanced fault detection
and diagnostic system is created followed by an implementation of the fault detection
and diagnostic system on a physical system. / Thesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nwu/oai:dspace.nwu.ac.za:10394/4110
Date January 2009
CreatorsLucouw, Alexander
PublisherNorth-West University
Source SetsSouth African National ETD Portal
Detected LanguageEnglish
TypeThesis

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