本研究探討風力發電機驅動鏈高速軸齒輪之故障診斷，研究的數據係基於3MW風力發電機之高速軸齒輪的振動訊號。由於加速規在監測齒輪狀態會受到其它組件振動訊號的干擾，風機葉片的旋轉也會因變動風速而改變，採用時間同步平均技術，將齒輪箱振動的採樣訊號與待分析齒輪的轉速同步進行疊加與平均，以消除其它組件訊號和外界雜訊。當齒輪箱發生故障時，邊帶相對於健康之齒輪會有明顯的振幅，我們藉由計算邊帶能量比判別齒輪故障程度，另並運用邊帶功率因數之計算以避免邊帶能量比因低峰值造成比值失真之缺點，而造成誤判之情況。本研究顯示經由適當的訊號處理，可大幅降低非同步訊號與雜訊之干擾並量化齒輪故障程度以供設備維修之重要參考。

This study investigates fault diagnosis of the high-speed shaft gear of the wind turbine drivetrain. The datasets studied are obtained from the vibration signals of the high-speed shaft gear of a 3MW wind turbine. The accelerometer monitoring the gear conditions may be disturbed by the vibration signals of other components and rotation of the turbine blades changes due to the variable wind speed. The time synchronous average technique is used to superimpose and average the sampled signal of the gearbox vibration according to the rotational speed of the gear to be analyzed, leading to the interference elimination of the other component signals and noises. When the gear is failing, the sideband amplitudes are more significant as compared to those of the healthy gear. We calculate the sideband energy ratio (SER) to determine the severity of gear faults, and use the sideband power factor (SBPF) to avoid the disadvantage of the ratio distortion which leads to misjudgment of the gear conditions, due to a low peak value at the gear mesh frequency in the calculation of the sideband energy ratio. This study shows that through proper signal processing, we can greatly reduce the interference of non-synchronous signals and noise. The severity of gear faults can be quantified to serve as an important reference for equipment maintenance.