The main focus of the work presented within this thesis was the testing and development of condition monitoring procedures for detection and diagnosis of HATT rotor imbalance faults. The condition monitoring processes were developed via Matlab with the goal of exploiting generator measurements for rotor fault monitoring. Suitable methods of turbine simulation and testing were developed in order to test the proposed CM processes. The algorithms were applied to both simulation based and experimental data sets which related to both steady-state and non-steady-state turbine operation. The work showed that development of condition monitoring practices based on analysis of data sets generated via CFD modelling was feasible. This could serve as a useful process for turbine developers. The work specifically showed that consideration of the torsional spectra observed in CFD datasets was useful in developing a, ‘rotor imbalance criteria’ which was sensitive to rotor imbalance conditions. Furthermore, based on the CFD datasets acquired it was possible to develop a parametric rotor model which was used to develop rotor torque time series under more general flow conditions. To further test condition monitoring processes and to develop the parametric rotor model developed based on CFD data a scale model turbine was developed. All aspects of data capture and test rig control was developed by the researcher. The test rig utilised data capture within the turbine nose cone which was synchronised with the global data capture clock source. Within the nose cone thrust and moment about one of the turbine blades was measured as well as acceleration at the turbine nose cone. The results of the flume testing showed that rotor imbalance criteria was suitable for rotor imbalance faults as applied to 4 generator quadrature axis current measurements as an analogue for drive train torque measurements. It was further found that feature fusion of the rotor imbalance criterion calculated with power coefficient monitoring was successful for imbalance fault diagnosis. The final part of the work presented was to develop drive train simulation processes which could be calculated in real-time and could be utilised to generate representative datasets under non-steady-state conditions. The parametric rotor model was developed, based on the data captured during flume testing, to allow for non-steady state operation. A number of simulations were then undertaken with various rotor faults simulated. The condition monitoring processes were then applied to the data sets generated. Condition monitoring based on operational surfaces was successful and normalised calculation of the surfaces was outlined. The rotor imbalance criterion was found to be less sensitive to the fault cases under non-steady state condition but could well be suitable for imbalance fault detection rather than diagnosis.