【摘要】 针对滚动轴承微弱故障特征提取困难,提出了基于共振稀疏分解与谱峭度的滚动轴承故障特征提取方法,根据滚动轴承故障振动信号中转频及谐波成分与周期性冲击成分的品质因子不同进行共振稀疏分解,得到包含转频及谐波等成分的高共振分量和包含故障特征的低共振分量,对低共振分量进行快速峭度图分析,得到包含故障特征的周期性冲击成分的频带范围,利用带通滤波器进行滤波,最后对滤波信号进行希尔伯特包络谱分析,提取出滚动轴承故障特征频率。实验结果表明该方法能有效地提取出表征滚动轴承故障特征的周期性冲击成分,剔除干扰成分,突出故障特征频率谱线,正确识别出滚动轴承的故障状态。更多还原

【Abstract】 Aiming at the difficulty in extracting the early weak fault features of rolling bearing, a fault feature extraction method based on resonance sparse decomposition and spectral kurtosis is proposed. According to the quality factor of each component in the rolling bearing fault vibration signal, the resonance sparse decomposition is performed by setting different quality factor, it is acquired that high resonance component included rotation frequency, harmonic, etc and low component containing the fault feature. The low resonance component are analyzed for the spectral kurtosis, and the frequency band range containing more fault characteristic components is obtained.The low resonance component is filtered by a band-pass filter, and then the Hilbert envelope spectrum analysis is performed on the filtered signal to extract the characteristic frequency of the rolling bearing fault.The simulation and experimental results show that the method can effectively extract the periodic impact components that characterize the fault characteristics of rolling bearings, eliminate the interference components, highlight the frequency spectrum of fault characteristics, and correctly identify the fault state of rolling bearing.更多还原