One of the world’s biggest concerns is global warming, a solution to this can be wind energy. Offshore wind energy has advantages over onshore wind energy, however, the levelized cost of energy is higher. The maintenance costs are a major cost contributor. To lower these costs, research is performed on faults and its detection. Currently, little is known about fault detectability and vibration propagation in a drive train of an offshore wind turbine. Fault detection and vibration propagation in a drive train of a 10 MW floating offshore wind turbine is therefore investigated to get an insight about the effect of faults on the vibration monitoring data of a drive train. Three different faults with five different degradation levels are applied one by one on the bearings of a 10 MW drive train model. These faults are radial and axial damage in the main shaft front bearing and radial damage in the high speed shaft rear bearing. One traditional, two non-traditional and two novel fault detection methods are used to detect faults and their vibration propagation. One common and one novel fault detection method are deployed in the time domain: the Velocity Root-Mean-Square (RMS) Threshold Method and the Peeters’ Anomaly Detection Method. The Velocity RMS Threshold Method compares the RMS of the vibration velocity of non-rotating parts with a threshold proposed by ISO 10816-21. The latter method makes use of statistical indicators and is tailored for this study. Although changes after fault introduction were observable, the methods can not be used and need to be altered for usage in the wind industry. The non-traditional Angular Velocity Error Energy Method is deployed in the frequency domain. It makes use of the angular velocity measurements from the drive train’s shafts and compares the normalized energy of its spectra with a threshold. This method inspired the development of novel fault detection methods introduced in this study, being the Bearing Velocity Energy Method (making use of bearing velocity measurements and also based on the Velocity Root-Mean-Square Threshold Method) and the Shaft Vibration Energy Method (making use of the velocity and acceleration of shafts). Both methods compare the normalized energy of the spectra with a threshold. Radial damage in the main shaft front bearing could be detected using the Angular Velocity Error Energy Method, the Bearing Velocity Energy Method and the Shaft Vibration Energy Method. Damage was detectable from 15% degradation onwards. Next to a change in vibration in the main shaft and its bearings, a different vibration behaviour was observed at the planet carrier front and rear bearing, intermediate speed shaft front bearing and on the low speed shaft. Axial damage in the main shaft front bearing could only be detected using the Shaft Vibration Energy Method. It was shown that this kind of damage was detectable by monitoring the main shaft’s vibration from 50% degradation and higher. Radial damage in the high speed shaft rear bearing could be detected using the Bearing Velocity Error Method and the Shaft Vibration Energy Method. Damage could only be detected for degradation higher than 70%, by monitoring the high speed shaft and its bearings. Next to the typical measurement locations, it is recommended to place extra sensors measuring velocity on the first stage planet carrier front and rear bearing housings, intermediate speed shaft front bearing housings and on the low speed shaft. The outcome of this study contributes to the understanding of vibration propagation and fault detection in a drive train. The fault detection methods can be implemented in maintenance and monitoring methods for offshore wind turbines. Maintenance engineers can use the detected vibration propagation to check the affected gearbox components and replace them before they fail.