Wind power has in the last 20 years grown into one of the main sources of renewable energy in the world, with both the amount and size of the turbines increasing substantially. One of the major challenges for the wind power industry is the premature failures of especially the drivetrain components. These failures cause a lot of turbine downtime, which increases the operation and maintenance costs of the turbines. Failures in the gearbox have been shown to lead to the highest downtime and the multitude of bearings within that subsystem is overrepresented in the total amount of component failures. Vibrationbased condition monitoring is considered the best method to find these types of defects early and avoid prolonged turbine downtime. Previous research has therefore been focused on the different aspects of condition monitoring; i.e. measurement technologies, signal analysis of vibration measurements to improve detection and diagnosis as well as the implementation of machine learning solutions. However, the majority of research work has yet to evaluate the performance of new developments using wind turbine field data, and still no fundamentally new developments have seen a large-scale implementation in the industry. Further, it is known that the positioning of the accelerometer, used to measure the vibrations, affects the ability to detect and diagnose defects. However, it is not known how to optimally position the accelerometers to monitor the individual drivetrain sub-systems. Also, previous research does not show how the influence of the measurement properties of the field data affect the ability to detect and diagnose component failures.

_{Therefore, this thesis provides a comprehensive evaluation of how to improve the detection and diagnosis of bearing failures specifically in wind turbine drivetrains. In this thesis, a simulation model was developed to study how the accelerometer positioning affects the measurement quality. Vibration simulations of a similar sized bearing to ones found in the wind turbine drivetrain show an optimal accelerometer position as close to the primary loaded zone of the bearings as possible. The current placement of the accelerometers in the wind turbine drivetrain are often diametrically opposed to the loaded zone, and the performance of the vibration monitoring with respect to the commonly used signal analysis tools could thereby be increased. Further, wavelet-based signal analysis has been evaluated using historical wind turbine drivetrain field data. A new implementation of the wavelet packet transform to analyse enveloped vibration measurements in the frequency domain was developed, where the measurements were decomposed into packets matching the frequency resolution of the fast Fourier transform, and analysing the packet energy spectra. Finally, an anomaly detection solution utilizing an artificial neural network has been implemented to separate the condition indicator values, used for detection and diagnosis, from their inherent variance due to the dynamic turbine operation seen in the drivetrain rotational speed.}

_{The results in this thesis show the inadequacy of the commonly stored vibration measurements to the condition monitoring databases when used in post failure investigations and application of research developments on available field data. Measurements both taken over a long period of time and covering wide frequency range should be stored, instead of the either/or of today. Otherwise, the real-time monitoring of wind turbine drivetrain bearing failures cannot be replicated and monitoring improvements not fully evaluated. By implementing the wavelet packet transform, the detection and diagnosis performance was increased 250% compared to the fast Fourier transform, in an example of gearbox output shaft bearing failure. By implementing the anomaly detection by the artificial neural network, the performance increased further and was able to provide indications in a planet bearing failure case, which was not possible before. For turbine owners, these results provide both practical actions to take and provide an example of an easily implementable signal analysis tool to improve the detection and diagnosis of drivetrain bearing failures. The anomaly detection, which utilizes available historic data from healthy turbines and does not require any amount of labelled data for all considered types of bearing failures, also shows promise to detect failures in the drivetrain components which has been historically problematic. For the research community, the results mainly provides guidance into using historic field data when evaluating new developments. Also, they highlight potential pitfalls one can face using field data and what data properties to look for to successfully show the potential of your work.}