S变换用于滚动轴承故障信号冲击特征提取

为从低信噪比的滚动轴承故障信号中提取出冲击特征，以便于进行轴承故障诊断，引入S变换的信号处理方法。以短时傅里叶变换（short time Fourier transform，简称STFT）以及连续小波变换（continuous wavelet transform，简称CWT）为理论基础，分别推导得出了连续S变换的定义式，并利用快速傅里叶变换（fast Fourier transform，简称FFT）实现S变换离散化计算。S变换克服了STFT时频分辨率固定的缺点，弥补了CWT缺乏相位信息的不足。仿真信号研究表明，S变换在信号整个频带上具有良好的时频分辨率和时频聚集性，能够提取低信噪比信号中的冲击特征，且性能优于STFT和CWT。最后对一组实际的滚动球轴承故障振动信号进行S变换处理，结果表明，S变换能够方便有效地从中提取出周期性的冲击特征，从而指导滚动轴承相关故障的诊断。

Impact Feature Extraction From Rolling Bearing Fault Signal by S Transform

In order to extract the impact feature from the rolling bearing fault signal in low signal-to noise ratio (SNR), which is important for bearing fault diagnosis, a signal processing method called S transform is introduced. Firstly, the definition formulas of S transform are derived from short time Fourier transform (STFT) and continuous wavelet transform (CWT), then discretized by fast Fourier transform (FFT). S transform successfully overcomes the disadvantage of fixed time-frequency resolution in STFT, as well as the lack of phase information in CWT. The simulation results show that S transform preserves good time-frequency resolution and concentration for all frequencies of the analyzed signal, making S transform effective in extracting the impact feature from the low SNR signal, with better performance than STFT and CWT. Finally, a physical fault vibration signal of the ball bearing is processed by S transform. The results verify the validity and practicability of S transform in the periodic impact feature extraction from the vibration signal, so as to guide related fault diagnosis for rolling bearings.