The Average-Log-Ratio, ALR, gear-damage detection algorithm is exercised on accelerometer recordings made during earlier-performed accelerated gear testing. Results of ALR computations from tooth bending-fatigue failure and pitting failure are displayed and discussed. The periodic behavior of the rotational-harmonic frequency spectra of tooth working-surface damage is verified and utilized in ALR detection of both tooth bending-fatigue and pitting damage. Rotational-harmonic frequency spectra out to the tenth tooth-meshing harmonic of damaged gears are utilized. ALR computations of gears failing in tooth-bending fatigue are shown to provide strong periodic contributions out to (and beyond) the tenth tooth-meshing harmonic, whereas tooth pitting damage is shown to generate rotational harmonic spectra with strongest contributions determined by the fractional size of the pitting damage on tooth working surfaces. This differing character of ALR rotational harmonic spectra between bending-fatigue and pitting damage allows remote detection of damage that can distinguish between these two classifications of gear damage.

平均对数比（ALR）齿轮损坏检测算法适用于在较早执行的加速齿轮测试期间产生的加速度计记录。显示并讨论了来自牙齿弯曲疲劳故障和点蚀故障的ALR计算结果。验证了牙齿工作表面损坏的旋转谐波频谱的周期性行为，并将其用于牙齿弯曲疲劳和点蚀的ALR检测中。利用了旋转谐波频谱直至损坏齿轮的第十次啮合啮合谐波。齿轮弯曲疲劳失效的ALR计算表明，齿轮第十次啮合谐波（以及超出第十次啮合）具有很强的周期性贡献，而点蚀损伤显示出产生的旋转谐波谱，其贡献最大，这取决于点蚀在牙齿工作面上的分数大小。弯曲疲劳和点蚀损伤之间的ALR旋转谐波频谱的这种不同特征允许远程检测损伤，从而可以区分这两种齿轮损伤分类。