一种改进的小波分析和Hilbert包络的轴承故障
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摘要
实现对滚动轴承的精确诊断,关键是选择调制性较好的特征频率区间并对所选区间的特征信号进行精细分析。针对这一问题,提出了基于改进后的小波分析和Hilbert包络普对滚动轴承的故障诊断。首先,对采集的故障轴承特征信号进行小波分解、降噪、重构,然后根据边带相关算法的基本思想在重构后的特征信号中得到滚动轴承特征信号的最佳调制信号区间,最后,对该区间内的特征信号用希尔伯特包络普进行包络分析,使其图形化呈现,并将其与正常轴承特征信号的包络图对比,由此判断是否出现故障。通过对SKF6203型滚动轴承的诊断,证明该诊断方法可以取得精确而又有效的诊断结果,为旋转类机械故障诊断分析提供了新思路。
To achieve accurate diagnosis of rolling bearings,the key is to select a better modulation of the characteristic frequency range and the selected interval of the characteristic signal for fine analysis. Aiming at this problem,a new fault diagnosis based on improved wavelet analysis and Hilbert envelope is proposed.Firstly,the characteristic signals of the faulted bearing are decomposed,reconstructed,Then,according to the basic idea of the sideband correlation algorithm,the optimal modulation signal interval of the rolling bearing characteristic signal is obtained in the reconstructed characteristic signal,Finally,the characteristic signal in this interval is analyzed by the Hilbert envelope and is graphically presented and compared with the envelope diagram of the normal bearing characteristic signal to judge whether it is faulty.Through the diagnosis of SKF6203 type rolling bearing,it is proved that the diagnosis method can obtain accurate and effective diagnosis result,which provides a new idea for the analysis of mechanical fault diagnosis.
To achieve accurate diagnosis of rolling bearings,the key is to select a better modulation of the characteristic frequency range and the selected interval of the characteristic signal for fine analysis. Aiming at this problem,a new fault diagnosis based on improved wavelet analysis and Hilbert envelope is proposed.Firstly,the characteristic signals of the faulted bearing are decomposed,reconstructed,Then,according to the basic idea of the sideband correlation algorithm,the optimal modulation signal interval of the rolling bearing characteristic signal is obtained in the reconstructed characteristic signal,Finally,the characteristic signal in this interval is analyzed by the Hilbert envelope and is graphically presented and compared with the envelope diagram of the normal bearing characteristic signal to judge whether it is faulty.Through the diagnosis of SKF6203 type rolling bearing,it is proved that the diagnosis method can obtain accurate and effective diagnosis result,which provides a new idea for the analysis of mechanical fault diagnosis.
引文
[1]胡耀斌,厉善元,胡良斌.基于神经网络的滚动轴承故障诊断方法的研究[J].机械设计与制造,2012(2):187-189.(Hu Yao-bin,Li Shan-yuan,Hu Liang-bin.Research on fault diagnosis method of rolling bearing based on neural network[J].Machinery Design&Manufacture,2012(2):187-189.)
[2]熊国良,胡俊锋,陈慧.基于SK-NLM包络的滚动轴承故障冲击特征增强[A].仪器仪表学报,2016.(Xiong Guo-liang,Hu Jun-feng,Chen Hui.Failure characteristics of rolling bearings based on SK-NLM envelope[A].Chinese Journal of Scientific Instrument,2016.)
[3]张晓涛,唐力伟,王平.轴承故障声发射信号多频带共振解调方法[J].振动/测试与诊断,2015,35(2):363-368.(Zhang Xiao-tao,Tang Li-wei,Wang Ping.Multi-band resonance demodulation method for acoustic emission signals of bearing[A].Journal of Vibration,Measurement&Diagnosis,2015,35(2):363-368.)
[4]张超,袁彦霞.频率调制经验模态分解在轴承故障诊断中的应用[A].振动与冲击,2014.(Zhang Chao,Yuan Yan-xia.Application of frequency modulation empirical mode decomposition in bearing fault diagnosis[A].Journal of Vibration and Shock,2014.)
[5]孙惠琴,钟原.小波分析与Hilbert变换在25T型客车故障诊断中的应用.(Sun Hui-qin,Zhong Yuan.Application of Wavelet Analysis and Hilbert Transform in Fault Diagnosis of 25T Bus.)
[6]PCJ Hill Techniques for Detectingand charactering covert communication signal[A].European Conference on Security and Detection[C].
[7]唐浩,屈梁生,温广瑞.基于Volterra级数的转子故障诊断研究[J]中国机械工程,2009,20(4):447-449.(Tang Hao,Qu Liang-sheng,Wen Guang-rui.Research on rotor fault diagnosis based on volterra series[J].China Mechanical Engineering,2009,20(4):447-449.)
[8]姜万录,张淑清,王益群.基于混沌和小波的故障信息诊断[M].北京:国防工业出版社,2005.(Jiang Wan-lu,Zhang Shu-qing,Wang Yi-qun.Fault Diagnosis Based on Chaos and Wavelet[M].BeiJing:National Defense Industry Press,2005.)
[9]李力,彭忠笑,彭书志.去趋势波动分析在齿轮故障诊断中的应用研究[J].中国机械工程,2009.(Li Li,Peng Zhong-xiao,Peng Shu-zhi.Applicationoftrendfluctuationanalysis in gear fault diagnosis[J].China Mechanical Engineering,2009.)
[10]唐卫辉,郭瑞峰,史丽晨.小波分析和Hilbert包络谱分析在故障诊断中应用.(Tang Wei-hui,Guo Rui-feng,Shi Li-chen.Application of Wavelet Analysis and Hilbert Envelope Spectrum Analysis in Fault Diagnosis.)
[11]何文平,吴琼,张文.滑动去趋势波动分析与近似熵在动力学结构突变检测中的性能比较[J].物理学报,2009,58(4).(He Wen-ping,Wu Qiong,Zhang Wen.Performance comparison of moving trend fluctuation analysis and approximate entropy in dynamic structure mutation detection[J].Journal of Physics,2009,58(4).)
[12]张淑清,陈白,张立国.小波分析算法研究及在齿轮与滚动轴承故障诊断中应用[J].传感技术学报,2007.(Zhang Shu-qing,Chen Bai,Zhang Li-guo.Research on wavelet analysis algorithm and its application in gear and roller bearing fault diagnosis[J].Chinese Journal of Sensors and Actuators,2007.)
[13]A Bashan,R Bartsch,J W Kantelhardt,S Havlin.Comparisonofdetrend ingmethods for fluctuationanalysis[J].Physical A,2008,387(21):5080-5090.
[14]卜伶俐,郭建英,蒋凤林.小波分析与Hilbert分析在滚动轴承故障诊断中的应用.(Pu Ling-li,G uo Jian-ying,Jiang Feng-lin.Application of Wavelet Analysis and Hilbert Analysis in Rolling Bearing Fault Diagnosis.)
[2]熊国良,胡俊锋,陈慧.基于SK-NLM包络的滚动轴承故障冲击特征增强[A].仪器仪表学报,2016.(Xiong Guo-liang,Hu Jun-feng,Chen Hui.Failure characteristics of rolling bearings based on SK-NLM envelope[A].Chinese Journal of Scientific Instrument,2016.)
[3]张晓涛,唐力伟,王平.轴承故障声发射信号多频带共振解调方法[J].振动/测试与诊断,2015,35(2):363-368.(Zhang Xiao-tao,Tang Li-wei,Wang Ping.Multi-band resonance demodulation method for acoustic emission signals of bearing[A].Journal of Vibration,Measurement&Diagnosis,2015,35(2):363-368.)
[4]张超,袁彦霞.频率调制经验模态分解在轴承故障诊断中的应用[A].振动与冲击,2014.(Zhang Chao,Yuan Yan-xia.Application of frequency modulation empirical mode decomposition in bearing fault diagnosis[A].Journal of Vibration and Shock,2014.)
[5]孙惠琴,钟原.小波分析与Hilbert变换在25T型客车故障诊断中的应用.(Sun Hui-qin,Zhong Yuan.Application of Wavelet Analysis and Hilbert Transform in Fault Diagnosis of 25T Bus.)
[6]PCJ Hill Techniques for Detectingand charactering covert communication signal[A].European Conference on Security and Detection[C].
[7]唐浩,屈梁生,温广瑞.基于Volterra级数的转子故障诊断研究[J]中国机械工程,2009,20(4):447-449.(Tang Hao,Qu Liang-sheng,Wen Guang-rui.Research on rotor fault diagnosis based on volterra series[J].China Mechanical Engineering,2009,20(4):447-449.)
[8]姜万录,张淑清,王益群.基于混沌和小波的故障信息诊断[M].北京:国防工业出版社,2005.(Jiang Wan-lu,Zhang Shu-qing,Wang Yi-qun.Fault Diagnosis Based on Chaos and Wavelet[M].BeiJing:National Defense Industry Press,2005.)
[9]李力,彭忠笑,彭书志.去趋势波动分析在齿轮故障诊断中的应用研究[J].中国机械工程,2009.(Li Li,Peng Zhong-xiao,Peng Shu-zhi.Applicationoftrendfluctuationanalysis in gear fault diagnosis[J].China Mechanical Engineering,2009.)
[10]唐卫辉,郭瑞峰,史丽晨.小波分析和Hilbert包络谱分析在故障诊断中应用.(Tang Wei-hui,Guo Rui-feng,Shi Li-chen.Application of Wavelet Analysis and Hilbert Envelope Spectrum Analysis in Fault Diagnosis.)
[11]何文平,吴琼,张文.滑动去趋势波动分析与近似熵在动力学结构突变检测中的性能比较[J].物理学报,2009,58(4).(He Wen-ping,Wu Qiong,Zhang Wen.Performance comparison of moving trend fluctuation analysis and approximate entropy in dynamic structure mutation detection[J].Journal of Physics,2009,58(4).)
[12]张淑清,陈白,张立国.小波分析算法研究及在齿轮与滚动轴承故障诊断中应用[J].传感技术学报,2007.(Zhang Shu-qing,Chen Bai,Zhang Li-guo.Research on wavelet analysis algorithm and its application in gear and roller bearing fault diagnosis[J].Chinese Journal of Sensors and Actuators,2007.)
[13]A Bashan,R Bartsch,J W Kantelhardt,S Havlin.Comparisonofdetrend ingmethods for fluctuationanalysis[J].Physical A,2008,387(21):5080-5090.
[14]卜伶俐,郭建英,蒋凤林.小波分析与Hilbert分析在滚动轴承故障诊断中的应用.(Pu Ling-li,G uo Jian-ying,Jiang Feng-lin.Application of Wavelet Analysis and Hilbert Analysis in Rolling Bearing Fault Diagnosis.)