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dc.contributor.authorLucouw, Alexander
dc.date.accessioned2011-04-08T12:10:58Z
dc.date.available2011-04-08T12:10:58Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10394/4110
dc.descriptionThesis (M.Ing. (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2009.
dc.description.abstractWith 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.
dc.publisherNorth-West University
dc.subjectFault detectionen
dc.subjectFault diagnosticsen
dc.subjectArtificial intelligenceen
dc.subjectFault model banken
dc.subjectRoboten
dc.titleDistributed fault detection and diagnostics using artificial intelligence techniquesen
dc.typeThesisen
dc.description.thesistypeMasters


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