关键词: 弱故障识别, 同步平均, 小波尺度谱, 旋转机械, 齿轮传动系统, 干摩擦阻尼环, 高速大功率密度, 减振特性

Abstract: Early fault feature of rotating machinery is weak and interfered by noise signal. It is difficult for accurate fault detection. The vibration signals are full of nonstationary characteristics when rotating machinery is in fault working condition. Different nonstationary characteristics are corresponding to different mechanical working conditions. Continuous wavelet transform analyses signal on multiple scales, which can describe the local characteristics of the signal in different scales. It is convenient to the fault signal detection. Time-domain synchronous average can weaken the random component of the observed signal, reduce the noise interference and extract the deterministic signals with the average cycle. The method of wavelet scalogram synchronous average is put forward on the basis of wavelet transform and synchronous average. Multi-cycle signal is processed by continuous wavelet transform and wavelet scalogram is reassigned. The scalogram is synchronous averaged, which can effectively suppress noise interference and identify the weak fault information. Both simulations and experiments investigation have been used to verify the effectiveness of this method. It can be concluded that this method will contribute to early fault diagnosis of rotating machinery.

Key words: Rotating machinery, Synchronous average, Wavelet scalogram, Weak fault recognition, Dry friction damping ring, High speed large power density, Vibration damping characteristics, Gear transmission