Health monitoring of a Brayton cycle-based power conversion unit / Carel P. du Rand
Du Rand, Carel Petrus
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The next generation nuclear power plants like the Pebble Bed Modular Reactor (PBMR) permit for the design of advanced health monitoring (fault diagnosis) systems to improve safety, system reliability and operational performance. Traditionally, fault diagnosis has been performed by applying limit value checking techniques. Although simple, the inability of these techniques to model parameter dependencies and detect incipient fault behaviour renders them unfavourable. More recent approaches to fault diagnosis can be attributed to the advances in computational intelligence. Data driven methods like artificial neural networks are more widely used when modelling complex nonlinear systems, using only historical plant data. These methods are however dependent on the quality and amount of data used for model development. The key to developing an advanced fault diagnosis system is to adopt an integrated approach for monitoring the different aspects of the total process. Within this context, this goal is realized by presenting a new integrated architecture for sensor fault diagnosis in addition to the enthalpy-entropy graph approach for process fault diagnosis. The integrated architecture for sensor fault diagnosis named SENSE, exploits the strengths of several existing techniques whilst reducing their individual shortcomings. A novel approach for process fault diagnosis is proposed based on the characteristics inherent in the design of the PBMR. Power control by means of an inventory control system and no bypass valve operation facilitates a reference model that remains invariant over the power range. Consequently, the devised reference fault signatures remain static during steady state and transient variations of the normal process. In the thesis, both single and multiple fault conditions are considered during steady state and transient variations of the normal process. It is demonstrated that by applying SENSE, the fused variable estimates are consistent and more accurate than the individual sensor readings. Test cases corresponding to 32 single and multiple fault conditions confirmed that it is possible to use the enthalpy-entropy graph approach for process fault diagnosis. In addition, the proposed fault diagnosis approach is validated through an application to real data from the prototype Pebble Bed Micro Model (PBMM) plant. This application demonstrated that the proposed approach is ideally suited for early detection of faults and greatly reduces the amount of plant data required for model development.
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