Abstract:
Today’s engineering systems have become increasingly more complex. This
makes fault diagnosis a more challenging task in industry and therefore a
significant amount of research has been undertaken on developing fault
diagnostic methodologies. So far there already exist a variety of diagnostic
methods, from qualitative to quantitative. However, no methods have
considered multi-component degradation when diagnosing faults at the system
level. For example, from the point a new aircraft takes off for the first time all of
its components start to degrade, and yet in previous studies it is presumed that
apart from the faulty component, other components in the system are operating
in a healthy state. This thesis makes a contribution through the development of
an experimental fuel rig to produce high quality data of multi-component
degradation and a probabilistic framework based on the Bayesian method to
diagnose faults in a system with considering multi-component degradation. The
proposed method is implemented on the fuel rig data which illustrates the
applicability of the proposed method and the diagnostic results are compared
with the neural network method in order to show the capabilities and
imperfections of the proposed method.
