Advances in high performance sensing technologies enable the development of wind turbine condition monitoring system to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based two stage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method and a graphical verification method together to ensure the diagnosis efficiency and accuracy. The efficacy of the proposed methodology is demonstrated with a case study with the gearbox condition monitoring Round Robin study dataset provided by the National Renewable Energy Laboratory (NREL). The developed methodology successfully picked five faults out of seven in total with accurate severity levels without producing any false alarm in the blind analysis. The case study results indicated that the developed fault detection framework is effective for analyzing gear and bearing faults in wind turbine drive train system based upon system vibration characteristics.