|dc.contributor.author||Madlopha, Lucky Sydney||
|dc.description||Thesis (M.Ing. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2006.||
|dc.description.abstract||The objective with this thesis is to investigate the potential of model-based diagnosis,
especially when combined with neural networks as modelling tool. The diagnosis system
has been applied to a model of the Pebble Bed Micro Model. The neural network was
mainly used as tool to simulate the normal behaviour of the plant.
The discrepancy between the two models (actual model and neural network) which
becomes larger when a fault is present is used to form residuals. The generation of
residuals needs to be followed by residual evaluation, in order to arrive at detection and
This thesis considers the design of fault detection and diagnosis for linear and nonlinear
systems. It consists of different sections. Firstly, an overview of the ideas and theory
behind the model-based approach of fault detection and diagnosis is given. Initially, a
fourth-order linear system is simulated and a number of faults are simulated, detected and
diagnosed. The knowledge gained with the first system is then refined and applied to a
nonlinear water level control system which is used as a benchmark. The calculations and
application results are presented in detail to illustrate the principles.
The principles are then applied to simulation as well as experimental results on the
Pebble Bed Micro Model. Flownex simulation software was used to generate the data.
where experimental data was not practical or safe to obtain.
Typical faults that were diagnosed are plant and instrumentation faults. Since the fullscale
Pebble Bed Modular Reactor plant is not yet in operation. the principles applied in
this thesis can be used to design and implement fault detection and diagnosis on a real
|dc.title||Aspects of total system fault detection and diagnosis using neural networks applied to the Pebble Bed Modular Reactor||en