利用小波尺度谱同步平均的时频脊故障特征提取

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。小波尺度谱存在受噪声干扰影响大、高频部分频率分辨率低等缺点, 对小波尺度谱进行重排可以提高其时频聚集性。为此，结合小波尺度谱同步平均和小波脊线分析的优点，提出了基于时频脊线特征提取方法。首先对多周期的振动信号进行小波连续变换，并重排小波尺度谱；再根据信号的周期性，对尺度谱进行同步平均；最后提取同步平均后的尺度谱小波脊线，计算信号的包络幅值并进行频谱分析，最终提取出弱故障特征。通过仿真和实例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。

Fault feature extraction for synchronous averaging wavelet scalogram based on time-frequency ridge

Early fault feature of rotating machinery is weak and interfered by noise, it is difficult for accurate early fault detection. Wavelet scalogram is affected by the noise, which has low frequency resolution for the high frequency part. Reassigned wavelet scalogram is applied to improve time-frequency concentration of the scalogram. Therefore, an weak fault feature extraction method is put forward based time frequency ridge by using synchronous averaging and wavelet ridge. Firstly, multi-cycle signal is processed by continuous wavelet transform and wavelet scalogram is reassigned. Then, the scalogram is synchronous averaged. After the wavelet ridge of scalogram is extracted, instantaneous amplitude curve of signal can be calculated, where frequency spectrum analysis will be used to extract fault characteristics. 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.