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中文摘要：
旋转机械的早期故障特征微弱，容易受到噪声的干扰，不容易准确识别。而旋转机械发生故障时其振动信号往往是非平稳信号，不同的非平稳性对应不同的故障状态。连续小波变换可以通过伸缩平移变换对信号进行多尺度细化分析，能够在不同的尺度上描述信号的局部特征，因此有利于故障信号的检测。时域同步平均可以削弱观测信号中的随机成分，降低噪声干扰，提取与平均周期相关的确定性信号，提高信噪比。结合小波变换和同步平均的优点，提出小波尺度谱同步平均的方法。对多周期的振动信号进行小波连续变换，并进行尺度谱重排，获得重排小波尺度谱：根据信号的周期性，对尺度谱进行同步平均，同步平均后的尺度谱可以有效地抑制干扰噪声，识别弱故障信息。通过仿真分析和实例分析验证了本方法的有效性，为旋转机械的早期故障诊断提供了新方法。
英文摘要：
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.
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