基于共振稀疏分解与谱峭度的滚动轴承故障诊断
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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。
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.
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.
引文
[1] 余发军,周凤星.基于可调Q因子小波变换和谱峭度的轴承早期故障诊断方法[J].中南大学学报(自然科学版),2015,46(11):4122-4128.
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[3] 安雪君,郝如江,史云林.基于SK-CICA和Teager能量算子的滚动轴承故障诊断方法[J].轴承,2018(10):42-46.
[4] 赵志宏,杨绍普.基于小波包变换与样本熵的滚动轴承故障诊断[J].振动、测试与诊断,2012,32(4):640-644.
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[8] 陈向民,于德介,罗洁思.基于信号共振稀疏分解的包络解调法及其在滚动轴承故障诊断中的应用[J].振动工程学报,2012,25(6):628-636.
[9] 张顶成,于德介,李星.滚动轴承故障诊断的品质因子可调小波重构方法[J].航空动力学报,2015,30(12):3051-3057.
[10] Antoni J.The spectral kurtosis:a useful tool for characterising non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[11] Selesnick I W.A new sparsity-enabled signal separation method based on signal resonance[C]// IEEE International Conference on Acoustics Speech and Signal Processing,2010.
[12] 张亢,程军圣.基于局部均值分解和峭度图的滚动轴承包络分析算法[J].航空动力学报,2015,30(12):3043-3050.
[2] 唐贵基,王晓龙.可调品质因子小波变换在滚动轴承微弱故障特征提取中的应用[J].中国电机工程学报,2016,36(3):746-754.
[3] 安雪君,郝如江,史云林.基于SK-CICA和Teager能量算子的滚动轴承故障诊断方法[J].轴承,2018(10):42-46.
[4] 赵志宏,杨绍普.基于小波包变换与样本熵的滚动轴承故障诊断[J].振动、测试与诊断,2012,32(4):640-644.
[5] Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].The Royal Society Proceedings A,1998,454(1971):903-995.
[6] Selesnick I W.Resonance-based signal decomposition:a new sparsity-enabled signal analysis method[J].Signal Processing,2011,91(12):2793-2809.
[7] Selesnick I W.Sparse signal representations using the tunable Q-factor wavelet transform[C]//Proceeding of SPIE Optical Engineering and Application.San Diego:international Society for Optics and Photonics,2011:81381.1-81381.15.
[8] 陈向民,于德介,罗洁思.基于信号共振稀疏分解的包络解调法及其在滚动轴承故障诊断中的应用[J].振动工程学报,2012,25(6):628-636.
[9] 张顶成,于德介,李星.滚动轴承故障诊断的品质因子可调小波重构方法[J].航空动力学报,2015,30(12):3051-3057.
[10] Antoni J.The spectral kurtosis:a useful tool for characterising non-stationary signals[J].Mechanical Systems and Signal Processing,2006,20(2):282-307.
[11] Selesnick I W.A new sparsity-enabled signal separation method based on signal resonance[C]// IEEE International Conference on Acoustics Speech and Signal Processing,2010.
[12] 张亢,程军圣.基于局部均值分解和峭度图的滚动轴承包络分析算法[J].航空动力学报,2015,30(12):3043-3050.