精细谱负熵及其在滚动轴承故障诊断中的应用

A Fine Spectral Negentropy Method and Its Application to Fault Diagnosis of Rolling Bearing

针对提取滚动轴承故障特征时共振边频带的中心频率难确定、带宽确定过宽或过窄及易受噪声影响等问题,提出了一种基于经验小波变换(EWT)的精细谱负熵方法(ASNE)。该方法利用经验小波滤波器特性构造滤波器组,实现对信号频域的扫描滤波;结合时域谱负熵更易检测信号中周期性冲击成分的特点,对滤波后的分量进行筛选,经过两次循环筛选获得了精确的中心频率和带宽;通过EWT提取出最佳的故障特征分量,最终进行包络解调获得故障特征信息。轴承内、外圈故障的实验信号对该方法进行了验证,实验结果表明,该方法能够快速、准确地确定共振边频带的中心频率与带宽,并有效地提取了内、外圈故障特征信息,且效果优于Infogram方法。该方法克服了传统边频带提取方法在频带划分上的局限性,抵制了噪声的干扰,提取的中心频率和带宽更精确,为更准确地判断轴承故障类型提供了可靠的理论支持。

A fine spectral negentropy (ASNE) method based on empirical wavelet transform (EWT) is proposed to solve the problems that it is difficult to determine the central frequency of the resonance sideband and the determination of the bandwidth is susceptible to noise when extracting fault features of rolling bearings. The proposed method constructs a filter bank by using the characteristics of empirical wavelet filter to realize the scanning filter in frequency domain. Then, the filtered components are screened by combining the feature of spectral negentropy in time domain, and it is easier to detect periodic impulse components in signals. The accurate central frequency and bandwidth are obtained after two scanning cycles. Then the optimum fault feature components are extracted through EWT, and the fault feature information of the bearing is finally obtained through envelope demodulation. The method is validated by the experimental signals of inner and outer races of rolling bearing. The results show that the method quickly and accurately determines the central frequency and bandwidth of resonance sideband, and effectively extracts the fault feature information of inner and outer races. The performance is better than that of the Infogram method. The proposed method overcomes the limitation of traditional method in frequency band division and immunity to noise, and extracts the central frequency and bandwidth more accurately.