Wind turbines (WTs) are designed to operate under extreme environmental conditions. This means that extreme and varying loads experienced by WT components need to be accounted for as well as gaining access to wind farms (WFs) at different times of the year. Condition monitoring (CM) is used by WF owners to assess WT health by detecting gearbox failures and planning for operations and maintenance (O&M). However, there are several challenges and limitations with commercially available CM technologies ranging from the cost of installing monitoring systems to the ability to detect faults accurately. This study seeks to address some of these challenges by developing novel techniques for fault detection using the RMS and Extreme (peak) values of vibration signals. The proposed techniques are based on three models (signal correlation, extreme vibration, and RMS intensity) and have been validated with a time domain data driven approach using CM data of operational WTs. The findings of this study show that monitoring RMS and Extreme values serves as a leading indicator for early detection of faults using Extreme value theory, giving WF owners time to schedule O&M. Furthermore, it also indicates that the prediction accuracy of each CM technique depends on the physics of failure. This suggests that an approach which incorporates the strengths of multiple techniques is needed for holistic health assessment of WT components. (C) 2016 The Authors. Published by Elsevier Ltd.