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dc.contributor.authorVorster, Christo
dc.date.accessioned2009-01-30T11:57:41Z
dc.date.available2009-01-30T11:57:41Z
dc.date.issued2004
dc.identifier.urihttp://hdl.handle.net/10394/254
dc.descriptionThesis (M.Ing. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2004.
dc.description.abstractModel-based fault detection and diagnostic systems have become an important solution (Munoz & Sanz-Bobi, 1998:178) in the industry for preventive maintenance. This not only increases plant safety, but also reduces down time and financial losses. This paper investigates a model-based fault detection and diagnostic system by using neural networks. To mimic process models, a normal feed-forward neural network with time delays is implemented by using the MATLAB@ neural network toolbox. By using these neural network models, residuals are generated. These residuals are then classified by using other neural networks. The main process in question is the Brayton cycle thermal process used in the pebble bed modular reactor. Flownet simulation software is used to generate the data, where practical data is absent. Various training algorithms were implemented and tested during the investigation of modelling and classification concepts on two benchmark processes. The training algorithm that performed best was finally implemented in an integrated concept
dc.publisherNorth-West University
dc.titleFault diagnostic system for predictive maintenance on a Brayton cycle power planten
dc.typeThesisen
dc.description.thesistypeMasters


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  • ETD@PUK [6400]
    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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