S变换用于滚动轴承故障信号冲击特征提取

Impact Feature Extraction From Rolling Bearing Fault Signal by S Transform

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。

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 timefrequency 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,aphysical 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.