With the increasing installation of the wind turbines both onshore and offshore, condition monitoring technologies and systems have become increasingly important in order to reduce the downtime and operations and maintenance (O&M) cost, thus maximising economic benefits. This paper presents a novel machine learning model-based data-driven approach to accurately evaluate the performance of the turbines and diagnose the faults. The approach is based on Long-short term memory (LSTM) incorporating a statistical tool named Kullback-Leibler divergence (KLD). The hybrid LSTM-KLD method has been applied to two faulty wind turbines with gearbox bearing fault and generator winding fault respectively for fault detection and identification. The proposed method is then compared with three other well-established machine-learning algorithms to investigate its superiority. The results show that the proposed method can produce a more effective detection with accuracy reaching 94% and 92% for the turbines, respectively. Furthermore, the proposed method can effectively distinguish the alarms from the faults, from which the distinguished alarms can be considered as an early warning of the fault occurrence.