Fault diagnosis for gearbox of wind turbine plays an important role in the normal operation of WT. Current studies commonly focus on diagnosis of fault types, nevertheless, in addition to identifying the fault type, the severity of the fault is also instructive for maintenance and repair for wind turbine. Thus, a novel optimized stacked diagnosis structure (OSDS) is proposed for identification of fault type and severity. Compressed sensing is adopted to implement compressed sampling of original vibration signals. Then, compressed samples are input into first and second layer deep belief networks ( DBNs) for identification of fault type and severity, separately. In addition, every single DBN in the OSDS is optimized with chaotic quantum particle swarm optimization ( CQPSO) algorithm. Comparison experiments based on bench mark gearbox fault data and working planetary gearbox show that the fault type diagnosis accuracy of this method reaches 99. 24% and 97. 21%, while the fault severity accuracy reaches 99. 06%. Meanwhile, the testing times are only 1. 493 and 2. 176 s.