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。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。

英文摘要：

Early fault feature of rotating machinery is weak and interfered by noises, so it is difficult for accurate early fault detection. Wavelet scalogram is sensitively affected by the noise and it has low resolution for its high frequency components. Reassigned wavelet scalogram is applied to improve time-frequency concentration of the scalogram. Therefore, a weak fault fea- ture extraction method is put forward based on time-frequency ridge by taking the advantage of the synchronous averaging and wavelet ridge. Firstly, multi-cycle signal is processed by continuous wavelet transformation and wavelet scalogram is reassigned. Then, the sealogram is synchronous averaged. After the wavelet ridge of scalogram is extracted, instantaneous amplitude curve of signal can be calculated, where the frequency spectrum analysis is 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.