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基于多尺度线调频基稀疏信号分解的广义解调方法及其在齿轮故障诊断中的应用 被引量:9

Generalized Demodulation Method Based on Multi-scale Chirplet and Sparse Signal Decomposition and Its Application to Gear Fault Diagnosis
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摘要 在基于多尺度线调频基稀疏信号分解的基础上,提出一种基于多尺度线调频基稀疏信号分解的广义解调方法,并将其应用于非平稳转速状态下的齿轮故障诊断。广义解调可以将时频分布呈曲线变化的多分量非平稳信号转化为时频分布平行于时间轴的平稳信号,因此非平稳信号经广义解调后满足傅里叶分析对平稳性的要求,而如何获取多分量信号的广义解调相位函数是广义解调方法的关键和难点。对信号进行基于多尺度线调频基的稀疏信号分解,得到分量信号的相位函数,再对分量信号进行广义解调和频谱分析得到齿轮故障特征频率。该方法非常适合于分析转速波动齿轮的多分量调幅—调频振动信号,仿真算例和应用实例说明了方法对变速齿轮箱故障诊断的有效性。 On the basis of multi-scale chirplet and sparse signal decomposition, a new method of generalized demodulation is proposed to be applied to the fault diagnosis of gear in a state of non-stationary rotating speed. The method of generalized demodulation can transform multi-component and non-stationary signals with time-frequency distribution consisting of curves into stationary signals with time-frequency distribution consisting of linear lines which are parallel to the time-axis. So after the implementation of generalized demodulation, the signals satisfy the standard of stationary demand in FFT analysis. The key point in generalized demodulation is how to obtain the phase functions of the multi-component signals. Through the sparse signal decomposition based on multi-scale chirplet of the signal, phase functions of the component signals are acquired and then generalized demodulation and frequency analysis are adopted to get the fault feature frequency components. The proposed method is suitable to analyze the multi-component AM - FM signals which fluctuate with the rotating speed. A simulation example and an example of its application prove the effectiveness of the method applied to fault diagnosis of the gearbox with rotating speed fluctuation.
出处 《机械工程学报》 EI CAS CSCD 北大核心 2010年第15期59-64,70,共7页 Journal of Mechanical Engineering
基金 国家自然科学基金(50875078) 国家高技术研究发展计划(863计划 2009AA04Z414) 湖南大学汽车车身先进设计制造国家重点实验室自主课题(60870002)资助项目
关键词 多尺度线调频基 稀疏信号分解 广义解调 齿轮 故障诊断 Multi-scale chirplet Sparse signal decomposition Generalized demodulation Gear Fault diagnosis
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参考文献14

  • 1GUO D,PENG Z K.Vibration analysis of a cracked rotor using Hilbert-Huang transform[J].Mechanical Systems and Signal Processing,2007,21(8):3 030-3 041.
  • 2CHOW T W S,SHI Hai.Induction machine fault diagnstic analysis with wavelet technique[J].IEEE Transactions on Industrial Electronics,2004,51(3):558-565.
  • 3HUANG N E,SHEN Z,LONG S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proc.R.Soc.Lond.A,1998(454):903-995.
  • 4CHEN Zhixin XU Jinwu YANG Debin.NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING[J].Chinese Journal of Mechanical Engineering,2008,21(4):87-91. 被引量:19
  • 5CHENG Junsheng,YU Dejie,TANG Jiashi,et al.Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis[J].Mechanism and Machine Theory,2008,43(6):712-723.
  • 6QIN S R,ZHONG Y M.A new algorithm of HilbertHuang transform[J].Mechanical Systems and Signal Processing,2006,20(8):1 941-1 952.
  • 7LEI Yaguo,HE Zhengjia,ZI Yanyang.Application of the EEMD method to rotor fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2009,23(4):1 327-1 338.
  • 8WU Fangji,QU Liangsheng.An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery[J].Journal of Sound and Vibration,2008,314 (3-5):586-602.
  • 9程军圣,于德介,杨宇.基于支持矢量回归机的Hilbert-Huang变换端点效应问题的处理方法[J].机械工程学报,2006,42(4):23-31. 被引量:75
  • 10OLHEDE S,WALDEN A T.A generalized demodulation approach to time-frequency projections for multicom-ponent signals[J].Proceedings of the Royal Society A,2005,461(2059):2 159-2 179.

二级参考文献28

  • 1程军圣,于德介,杨宇.基于EMD的能量算子解调方法及其在机械故障诊断中的应用[J].机械工程学报,2004,40(8):115-118. 被引量:85
  • 2DuanChendong HeZhengjia JiangHongkai.NEW METHOD FOR WEAK FAULT FEATURE EXTRACTION BASED ON SECOND GENERATION WAVELET TRANSFORM AND ITS APPLICATION[J].Chinese Journal of Mechanical Engineering,2004,17(4):543-547. 被引量:12
  • 3刘芳,刘文学,焦李成.基于复小波邻域隐马尔科夫模型的图像去噪[J].电子学报,2005,33(7):1284-1287. 被引量:13
  • 4HUANG N E, SHEN Z, LONG S R. The Empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond.A, 1998,(454):903-995.
  • 5HUANG N E, SHEN Z, LONG S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annu. Rev.Fluid Mech., 1999, 31:417-457.
  • 6VINCENT H T, HU S L J, HOU Z. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis[C]//Proceedings of the Second International Workshop on Structural Health Monitoring.Stanford, 1999: 891-900.
  • 7GRENIER Y. Time-dependent ARMA modeling of nonstationary signal[J]. IEEE Trans. on ASSP, 1983, 31(4):899-911.
  • 8BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998,2(2): 121-167.
  • 9CAO L J. Support vector machines experts for time series forecasting[J]. Neurocomputing, 2003,51: 321-339.
  • 10TAY F E H, CAO L J. Modified support vector machines in financial time series forecasting[J]. Neurocomputing,2002, 48: 847-861.

共引文献106

同被引文献47

  • 1卿宗胜,高云鹏,吴聪,杨佳伟,王庆凯.基于自适应VMD和改进功率谱估计的球磨机负荷特征提取[J].仪器仪表学报,2020(5):234-241. 被引量:29
  • 2屈文涛,沈允文,徐建宁,赵宁.双圆弧齿轮传动的温度场和热变形分析[J].石油机械,2006,34(3):13-15. 被引量:10
  • 3段礼祥,张来斌,王朝晖,张东亮.柴油机振动信号的小波包奇异值降噪[J].中国石油大学学报(自然科学版),2006,30(1):93-97. 被引量:22
  • 4LIU B,LING S F,GRIBONVAL R.Bearing failuredetection using matching pursuit[J].NDT&EInternational,2002,35(4):255-262.
  • 5DONG Hongbo,CHEN Xuefeng,LI Bi,et al.Rotorcrack detection based on high-precision modal parameteridentification method and wavelet finite elementmodel[J].Mechanical Systems and Signal Processing,2009,23(3):869-883.
  • 6WANG Shibin,HUANG Weiguo,ZHU Zhongkui.Transient modeling and parameter identification based onwavelet and correlation filtering for rotating machinefault diagnosis[J].Mechanical Systems and SignalProcessing,2011,25(4):1299-1320.
  • 7RANDALL R B,ANTONI J.Rolling element bearingdiagnostics—a tutorial[J].Mechanical Systems andSignal Processing,2011,25(2):485-520.
  • 8ANTONI J,RANDALL R B.Differential diagnosis ofgear and bearing faults[J].ASME Journal of Vibrationand Acoustics,2002,124:165-171.
  • 9The Case Western Reserve University Bearing DataCenter.Bearing data center seeded fault test data[EB/OL].[2009-09-11].http://www.eecs.cwru.edu/laboratory/bearing.
  • 10祁克玉,向家伟,訾艳阳,何正嘉.基于Laplace小波相关滤波的结构模态参数精确识别方法[J].机械工程学报,2007,43(9):167-172. 被引量:15

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