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基于改进时频谱分析方法的滚动轴承复合故障诊断

王宏超 向国权 郭志强 巩晓赟 杜文辽

王宏超, 向国权, 郭志强, 巩晓赟, 杜文辽. 基于改进时频谱分析方法的滚动轴承复合故障诊断[J]. 航空动力学报, 2017, 32(7): 1698-1703. doi: 10.13224/j.cnki.jasp.2017.07.021
引用本文: 王宏超, 向国权, 郭志强, 巩晓赟, 杜文辽. 基于改进时频谱分析方法的滚动轴承复合故障诊断[J]. 航空动力学报, 2017, 32(7): 1698-1703. doi: 10.13224/j.cnki.jasp.2017.07.021
Fault diagnosis of rolling bearing’ compound faults based on improved time-frequency spectrum analysis method[J]. Journal of Aerospace Power, 2017, 32(7): 1698-1703. doi: 10.13224/j.cnki.jasp.2017.07.021
Citation: Fault diagnosis of rolling bearing’ compound faults based on improved time-frequency spectrum analysis method[J]. Journal of Aerospace Power, 2017, 32(7): 1698-1703. doi: 10.13224/j.cnki.jasp.2017.07.021

基于改进时频谱分析方法的滚动轴承复合故障诊断

doi: 10.13224/j.cnki.jasp.2017.07.021
基金项目: 国家青年自然科学基金(51405453,51205371); 郑州轻工业学院博士科研基金资助项目

Fault diagnosis of rolling bearing’ compound faults based on improved time-frequency spectrum analysis method

  • 摘要: 将基于循环平稳理论及2阶循环统计量的谱相关或谱相关密度分析方法加以改进,提出一种时频分析方法并将其用于滚动轴承发生复合故障时调制现象循环调制频率即故障特征频率的提取。通过对滚动轴承复合故障的仿真及实际实验振动数据进行分析,结果表明:与同时提取出调制频率和载频的传统包络解调谱分析方法不同,改进的谱分析方法可以只提取出调制频率,提取的谱结构分布具有更清晰的表达效果,从而为滚动轴承的复合故障特征提取提供一种方法。

     

  • [1] ANTONI J.Cyclic spectral analysis of rolling-element bearing signals:facts and fictions[J].Journal of Sound and Vibration,2007,304(3/4/5):497-529.
    [2] DONG G M,CHEN J.Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2012,33(2):212-236.
    [3] KANKAR P K,SHARMA S C,HARSHA S P.Fault diagnosis of rolling bearing using cyclic autocorrelation and wavelet transform[J].Neurocomputing,2013,110(8):9-17.
    [4] PURUSHOTHAM V,NARAYANAN S,PRASAD S S A N.Multi-fault diagnosis of rolling bearing elements using wavelet analysis and Hidden Markov model based fault recognition[J].NDT and E International,2005,38(8):654-664.
    [5] ABBASION S,RAFSANJANI A,FARSHIDIANFAR A,et al.Rolling element bearings multi-fault classification based on wavelet denoising and support vector mahine[J].Mechanical Systems and Signal Processing,2007,21(7):2933-2945.
    [6] LEI Y G,HE Z J,ZI Y Y.Application of a novel hybrid intelligent method to compound fault diagnosis of locomotive roller bearings[J].Journal of Vibration and Acoustics,2008,130(3):034501.1-034501.6.
    [7] VOKELJ M,ZUPAN S,PREBIL I.Multivariate and multiscal monitoring of large-size low-speed bearings using ensemble mode decomposition method combined with principal component analysis[J].Mechanical Systems and Signal Processing,2010,24(1):1049-1067.
    [8] LI Z X,YAN X P,TIAN Z,et al.Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for gearbox multi-fault diagnosis[J].Measurement,2013,46(1):259-271.
    [9] 陈果.滚动轴承表明损伤故障智能诊断新方法[J].仪器仪表学报,2009,30(1):44-49.CHEN Guo.New intelligent diagnosis method for ball bearing faults due to surface damage[J].Chinese Journal of Scientific Instrument,2009,30(1):44-49.(in Chinese)
    [10] 陈果.滚动轴承早期故障的特征提取与智能诊断[J].航空学报,2009,30(2):362-367.CHEN Guo.Feature extraction and intelligent diagnosis for ball bearing early faults[J].Acta Aeronautica et Astronautica Sinca,2009,30(2):362-367.(in Chinese)
    [11] 王波,刘树林, 蒋超,等.基于量子遗传算法优化RVM的滚动轴承智能故障诊断[J].振动与冲击,2015,34(17):207-212.WANG Bo,LIU Shulin,JIANG Chao,et al.Rolling bearings intelligent fault diagnosis based on RVM optimized with quantum genetic algorithm[J].Journal of Vibration and Shock,2015,34(17):207-212.(in Chinese)
    [12] 谢三毛.基于时变自回归模型与神经网络的滚动轴承故障智能诊断[J].轴承,2014(10):43-46.XIE Sanmao.Fault intelligent diagnosis for rolling bearing based on time varying autoregressive model and neural network[J].Bearing,2014(10):43-46.(in Chinese)
    [13] FENG Z P,LIANG M,CHU F L.Recent advances in time-frequency analysis methods for machinery fault diagnosis:a review with application examples[J].Mechanical Systems and Signal Processing,2013,38(1):165-205.
    [14] 毕果,陈进,周福昌,等.调幅信号谱相关密度分析中白噪声影响的研究[J].振动与冲击,2006,25(2):75-78.BI Guo,CHEN Jin,ZHOU Fuchang,et al.Influence of the noise on spectral correlation density analysis of am signal[J].Journal of vibration and shock,2006,25(2):75-78.(in Chinese)
    [15] ANTONI J,BONNARDOT F,RAAD A,et al.Cyclostationary modeling of rotating machine vibration signals[J].Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
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出版历程
  • 收稿日期:  2015-08-21
  • 刊出日期:  2017-07-28

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