Aspects of total system fault detection and diagnosis using neural networks applied to the Pebble Bed Modular Reactor

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 isolation decisions. 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 system.