基于变分模态分解法和共振解调技术的滚动轴承早期故障检测研究
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摘要
针对滚动轴承早期故障信号提取困难的问题,基于变分模态分解法和共振解调技术,对滚动轴承早期故障检测进行研究。采用变分模态分解法对滚动轴承振动信号进行分解,计算各分解分量的峭度值,并选取两个最敏感的固有模态分解分量进行重构,然后利用共振解调技术进行解调分析,采用快速傅里叶变换计算出包络谱图。试验结果表明,应用变分模态分解法与共振解调技术更能准确地判断出滚动轴承的早期故障。
Aiming at the difficulty of early extraction of fault signal for rolling bearings, based on VMD method and resonance demodulation technology, the early fault detection of rolling bearings was studied.The VMD method was used to decompose the vibration signal of the rolling bearing and calculate the kurtosis value of each decomposition component while two most sensitive IMFs were selected for reconstruction, and then the resonance demodulation technique was used for demodulation analysis. The FFT was adopted for calculation of the envelope spectrogram. The test results show that the application of VMD method and resonance demodulation technology can more accurately determine the early failure of rolling bearings.
Aiming at the difficulty of early extraction of fault signal for rolling bearings, based on VMD method and resonance demodulation technology, the early fault detection of rolling bearings was studied.The VMD method was used to decompose the vibration signal of the rolling bearing and calculate the kurtosis value of each decomposition component while two most sensitive IMFs were selected for reconstruction, and then the resonance demodulation technique was used for demodulation analysis. The FFT was adopted for calculation of the envelope spectrogram. The test results show that the application of VMD method and resonance demodulation technology can more accurately determine the early failure of rolling bearings.
引文
[1]周智,朱永生,张优云,等.基于EEMD和共振解调的滚动轴承自适应故障诊断[J].振动与冲击,2013,32(2):76-80.
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[5]DWYER R F. Detection of Non-Gaussian Signals by Frequency Domain Kurtosis Estimation[C]. ICASSP′83. IEEE International Conference on Acoustics, Speech, and Signal Processing,Boston,1983.
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[8]王晓龙,唐贵基.基于变分模态分解和1.5维谱的轴承早期故障诊断方法[J].电力自动化设备,2016,36(7):125-130.
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[10]郑小霞,周国旺,任浩翰,等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击,2017,36(22):22-28.
[11]严斌,韦尧兵,剡昌锋,等.基于改进形态分量分析的滚动轴承包络分析方法[J].机械制造,2016,54(11):84-88,92.
[2]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]. Proceedings of the Royal Society A:Mathematical Physical and Engineering Sciences,1998,454:903-995.
[3]DRAGOMIRETSKIY K,ZOSSO D.Variational Mode Decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[4]唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-81.
[5]DWYER R F. Detection of Non-Gaussian Signals by Frequency Domain Kurtosis Estimation[C]. ICASSP′83. IEEE International Conference on Acoustics, Speech, and Signal Processing,Boston,1983.
[6]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3358-3365.
[7]唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638-648.
[8]王晓龙,唐贵基.基于变分模态分解和1.5维谱的轴承早期故障诊断方法[J].电力自动化设备,2016,36(7):125-130.
[9]郑近德,程军圣,曾鸣,等.广义经验模态分解性能分析与应用[J].振动与冲击,2015,34(3):123-128,155.
[10]郑小霞,周国旺,任浩翰,等.基于变分模态分解和排列熵的滚动轴承故障诊断[J].振动与冲击,2017,36(22):22-28.
[11]严斌,韦尧兵,剡昌锋,等.基于改进形态分量分析的滚动轴承包络分析方法[J].机械制造,2016,54(11):84-88,92.