基于振动模态分析的轴承缺陷自动监测系统研究
|
摘要
通过分析轴承振动模态间的包络特性,设计一种轴承缺陷的自动监测系统,该系统选取受监测轴承的两个振动模态,设计类比解调电路来获得振动模态的包络信号,进而通过ARM芯片设计来建立自动监测系统,以分析诊断轴承的缺陷。然后通过Filter法来设计振动信号的解调分析电路,此解调分析电路先经由可设定中心频率的带通滤波器,以滤出所选取的两个不同模态的振动信号后,再经过精密全波整流、低通滤波信号处理,以获得两振动模态的包络信号。由于其模态包络信号间具有密切相关性,通过计算包络信号间的相关系数(阀值0.4),并应用于轴承缺陷的实时诊断,实验表明该方法具有较好的可行性及正确性。
By analyzing the envelope characteristics between the vibration modes of the bearing,an automatic monitoring system for bearing defects is designed.Two vibration modes were first designated and demodulated to derive the envelope of the two vibration mode.Then,an ARMsingle-chip microprocessor was applied to analyze the vibration envelopes for setting-up an automatic monitoring system.In the study,a demodulating circuit based on the Filter-based Hilbert transform was designed for the envelope detection.Two ICs were applied for band-pass Filtering to Filter out the two mode vibrations.Then,the band-passed signals were processed through two circuits for full-wave rectification.Finally,the envelope signal of the mode vibration could be derived from passing the rectified signal through a low-pass Filter.It is found that the correlation coefficient between the envelopes of two mode vibrations for defect bearing is higher than that for normal one.Accordingly,the correlation coefficient(0.4) between the vibration envelopes could be applied to be a quantitative index for the bearing defect diagnosis.Thus,it is proved that the proposed diagnosis devicecould be effectively applied in the defect diagnosis for the bearing system.Experiments show that this system has good feasibility and correctness.
By analyzing the envelope characteristics between the vibration modes of the bearing,an automatic monitoring system for bearing defects is designed.Two vibration modes were first designated and demodulated to derive the envelope of the two vibration mode.Then,an ARMsingle-chip microprocessor was applied to analyze the vibration envelopes for setting-up an automatic monitoring system.In the study,a demodulating circuit based on the Filter-based Hilbert transform was designed for the envelope detection.Two ICs were applied for band-pass Filtering to Filter out the two mode vibrations.Then,the band-passed signals were processed through two circuits for full-wave rectification.Finally,the envelope signal of the mode vibration could be derived from passing the rectified signal through a low-pass Filter.It is found that the correlation coefficient between the envelopes of two mode vibrations for defect bearing is higher than that for normal one.Accordingly,the correlation coefficient(0.4) between the vibration envelopes could be applied to be a quantitative index for the bearing defect diagnosis.Thus,it is proved that the proposed diagnosis devicecould be effectively applied in the defect diagnosis for the bearing system.Experiments show that this system has good feasibility and correctness.
引文
[1]刘国云,曾京,罗仁,等.轴箱轴承缺陷状态下的高速车辆振动特性分析[J].振动与冲击,2016,35(9):37-42.
[2]许立新,杨玉虎.一种考虑轴承缺陷影响的机构动力学分析方法[J].机械工程学报,2015,51(19):101-108.
[3]吴雅朋,蒋章雷,王吉芳,等.改进小波降噪算法在轴承缺陷图像的应用[J].组合机床与自动化加工技术,2017(11):65-68.
[4]Sheen Y T,Hung C K.Constructing a wavelet-based envelope function for vibration signal analysis[J].Mechanical Systems&Signal Processing,2004,18(1):119-126.
[5]Sun G,Zhang Q,Qing A,et al.A Vibration Signal Envelope Extract Method Based on Wavelet[J].International Journal of Smart Home,2015,9(2):265-274.
[6]Ghods A,Lee H H.Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors[J].Neurocomputing,2016,188:206-216.
[7]王淼清,顾军,刘青.基于边带相关算法和Hilbert包络谱的滚动轴承故障诊断[J].机械传动,2016(6):144-148.
[8]张西宁,李兵,雷威.一种改进的局部倒频谱分析方法[J].西安交通大学学报,2016,50(8):1-5.
[9]唐先广,郭瑜,丁彦春,等.基于短时傅里叶变换和独立分量分析的滚动轴承包络分析[J].机械强度,2012,34(1):5-9.
[10]何贞志,陈於学,邵明辉,等.基于Lab VIEW的滚动轴承缺陷在线智能检测系统[J].仪表技术与传感器,2017(7):72-75.
[11]Heng R B W,Nor M J M.Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition[J].Applied Acoustics,1998,53(1-3):211-226.
[12]Selesnick I W.Hilbert transform pairs of wavelet bases[J].IEEE Signal Processing Letters,2001,8(6):170-173.
[13]Hilbert S.Correlation Coefficient[M].Springer International Publishing,2017.
[2]许立新,杨玉虎.一种考虑轴承缺陷影响的机构动力学分析方法[J].机械工程学报,2015,51(19):101-108.
[3]吴雅朋,蒋章雷,王吉芳,等.改进小波降噪算法在轴承缺陷图像的应用[J].组合机床与自动化加工技术,2017(11):65-68.
[4]Sheen Y T,Hung C K.Constructing a wavelet-based envelope function for vibration signal analysis[J].Mechanical Systems&Signal Processing,2004,18(1):119-126.
[5]Sun G,Zhang Q,Qing A,et al.A Vibration Signal Envelope Extract Method Based on Wavelet[J].International Journal of Smart Home,2015,9(2):265-274.
[6]Ghods A,Lee H H.Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors[J].Neurocomputing,2016,188:206-216.
[7]王淼清,顾军,刘青.基于边带相关算法和Hilbert包络谱的滚动轴承故障诊断[J].机械传动,2016(6):144-148.
[8]张西宁,李兵,雷威.一种改进的局部倒频谱分析方法[J].西安交通大学学报,2016,50(8):1-5.
[9]唐先广,郭瑜,丁彦春,等.基于短时傅里叶变换和独立分量分析的滚动轴承包络分析[J].机械强度,2012,34(1):5-9.
[10]何贞志,陈於学,邵明辉,等.基于Lab VIEW的滚动轴承缺陷在线智能检测系统[J].仪表技术与传感器,2017(7):72-75.
[11]Heng R B W,Nor M J M.Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition[J].Applied Acoustics,1998,53(1-3):211-226.
[12]Selesnick I W.Hilbert transform pairs of wavelet bases[J].IEEE Signal Processing Letters,2001,8(6):170-173.
[13]Hilbert S.Correlation Coefficient[M].Springer International Publishing,2017.