基于Hilbert-Huang变换的齿轮箱故障诊断
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摘要
齿轮箱传动是机械设备中最常见的传动方式之一,同时,齿轮箱是设备中容易失效的部件,它的异常又是诱发机器故障的重要原因,因此对齿轮箱进行准确、及时的故障诊断是非常重要的。以往常用于故障诊断的方法有傅里叶变换,短时傅立叶变换、Wigner-Ville分布、小波变换方法。但是这些方法都是基于傅里叶变换为基础,对平稳信号的分析有很大优势,而对瞬时时变信号不适应,无法得到信号各频率分量随时间的变化关系。
针对故障时振动信号的非线性、非平稳性,论文采用Hilbert-Huang变换的时频分析方法。Hilbert-Huang变换方法是一种新的信号处理方法,包括经验模式分解(EMD)和Hilbert变换两部分。它将信号分解为有限个数的本征模函数(IMF)之和,然后对每个IMF进行Hilbert变换,得到Hilbert谱,具有很高的时频分辨率。论文重点分析了Hilbert-Huang变换的基本理论、算法过程和物理意义,并通过仿真例子说明方法的优越性。
针对齿轮箱的振动机理,典型的故障特征,以及常用的故障诊断方法等因素,把Hilbert-Huang变换方法应用于提升机齿轮箱的故障诊断中。首先通过简易的振动测试,判断齿轮箱有故障的部位,然后再用Hilbert-Huang变换进行精密的诊断。通过分析对比得到的幅值谱,Hilbert-Huang谱图,以及边际谱图,成功的诊断出齿轮的磨损,以及齿轮之间存在调制影响的故障。
Gearbox transmission is one of the most popular in the machinery and equipment. At the same time, gearbox is the easy inactivation part in equipment and its abnormal is also the important factor to inducing failure of machinery. So it is very critical to diagnose gearbox faults accurately and quickly. The method we use in fault diagnose include Fourier Transform, Short Time Fourier Transform (STFT), Wigner-Vill Distribution and Wavelet Analysis usually. But these methods are base on Fourier Transform, and predominance to analyze the stable signal not the instantaneous. It can not get the signal instant frequency with temporal change.
In view of the fault vibration signal non-linearity, non-stationary, the paper uses the Hilbert-Huang Transform time-frequency analysis method. The Hilbert-Huang Transform is a new method for analyzing signal, consists of two successive parts, the Empirical Mode Decomposition (EMD) and the Hilbert transform. It decomposes the complicated signal into a number of Intrinsic Mode Function (IMF), and then the Hilbert transform is performed on each IMF, and the Hilbert spectra of all IMFs are grouped to get the Hilbert spectrum of the original signal, which possesses high time-frequency resolution. So the key point of paper analyzes the basic principle, the arithmetic, and the physics significance of Hilbert-Huang Transform, shows the superiority though emulation example.
Against the vibration mechanics, the typical fault feature of gearbox and the common fault diagnosis method, the Hilbert-Huang Transform is applied in elevator conveyor gearbox fault diagnosis. Firstly identify fault position of gearbox though simplify vibration measurement, and then use the Hilbert-Huang Transform to diagnosis delicately. According to analyze, compare the magnitude spectrum, the Hilbert-Huang spectrum, and the marginal spectrum, diagnosis the fault of gear abrasion and modulation effect between gears successfully.
针对故障时振动信号的非线性、非平稳性,论文采用Hilbert-Huang变换的时频分析方法。Hilbert-Huang变换方法是一种新的信号处理方法,包括经验模式分解(EMD)和Hilbert变换两部分。它将信号分解为有限个数的本征模函数(IMF)之和,然后对每个IMF进行Hilbert变换,得到Hilbert谱,具有很高的时频分辨率。论文重点分析了Hilbert-Huang变换的基本理论、算法过程和物理意义,并通过仿真例子说明方法的优越性。
针对齿轮箱的振动机理,典型的故障特征,以及常用的故障诊断方法等因素,把Hilbert-Huang变换方法应用于提升机齿轮箱的故障诊断中。首先通过简易的振动测试,判断齿轮箱有故障的部位,然后再用Hilbert-Huang变换进行精密的诊断。通过分析对比得到的幅值谱,Hilbert-Huang谱图,以及边际谱图,成功的诊断出齿轮的磨损,以及齿轮之间存在调制影响的故障。
Gearbox transmission is one of the most popular in the machinery and equipment. At the same time, gearbox is the easy inactivation part in equipment and its abnormal is also the important factor to inducing failure of machinery. So it is very critical to diagnose gearbox faults accurately and quickly. The method we use in fault diagnose include Fourier Transform, Short Time Fourier Transform (STFT), Wigner-Vill Distribution and Wavelet Analysis usually. But these methods are base on Fourier Transform, and predominance to analyze the stable signal not the instantaneous. It can not get the signal instant frequency with temporal change.
In view of the fault vibration signal non-linearity, non-stationary, the paper uses the Hilbert-Huang Transform time-frequency analysis method. The Hilbert-Huang Transform is a new method for analyzing signal, consists of two successive parts, the Empirical Mode Decomposition (EMD) and the Hilbert transform. It decomposes the complicated signal into a number of Intrinsic Mode Function (IMF), and then the Hilbert transform is performed on each IMF, and the Hilbert spectra of all IMFs are grouped to get the Hilbert spectrum of the original signal, which possesses high time-frequency resolution. So the key point of paper analyzes the basic principle, the arithmetic, and the physics significance of Hilbert-Huang Transform, shows the superiority though emulation example.
Against the vibration mechanics, the typical fault feature of gearbox and the common fault diagnosis method, the Hilbert-Huang Transform is applied in elevator conveyor gearbox fault diagnosis. Firstly identify fault position of gearbox though simplify vibration measurement, and then use the Hilbert-Huang Transform to diagnosis delicately. According to analyze, compare the magnitude spectrum, the Hilbert-Huang spectrum, and the marginal spectrum, diagnosis the fault of gear abrasion and modulation effect between gears successfully.
引文
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[2]屈梁生,何正嘉.机械故障诊断学[M]上海:上海科学技术出版社,1986.
[3]徐敏,虞和济,张瑞林等.设备故障诊断手册—机械设备状态监测和故障诊断[M].西安:西安交通大学出版社, 1998.
[4]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社, 1997
[5]卢文祥,杜润生.机械工程测试.信息.信号分析(第二版) [M].武汉:华中科技大学出版社, 1999
[6]胡广书.数字信号处理——理论、算法与实现[M].北京:清华大学出版社, 1997.
[7]张贤达,现代信号处理(第二版)[M]北京:清华大学出版社, 2003.
[8]张贤达,保铮.非平稳信号分析处理[M]北京:国防工业出版社, 1998.
[9] L.Cohen著,白居宪译.时频分析理论与应用[M]西安:西安交通大学出版社, 1998.
[10]陈进.机械设备故障诊断与现代信号处理技术[J].振动工程学报, 1998 11(5):7-10
[11]刘林.时频分析理论和应用[J].计算机自动测量与控制, 2001,9(4): 44-47
[12]吴正国,夏立,尹为民.现代信号处理技术[M].武汉:武汉大学出版社,2002.
[13]胡昌华,张军波等.基于Matlab的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社,1999.
[14] Huang N E,Shen Z,Long S R,et a1.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society London.A,1998,454,903-995.
[15] N. E. Huang, M. Wu, S. Long et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proc. Royal Soc. London. A, 2003, 459(2307):2317-2345.
[16] N. E.Huang, Z.Shen, S. Long. A new view of nonlinear water waves: The Hilbert Spectrum[J]. Annu. Rev. Fluid Mech. 1999, 31(1): 417~457.
[17] G. Rilling, P. Flandrin. On empirical mode decomposition and its algorithms[J]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03, Grado(I), June 2003
[18] G. Rilling, P. Flandrin. Bivariate Empirical Mode Decomposition[J]. Signal Processing .Letter 2006.
[19] R. Deering and J. F. Kaiser The use of a masking signal to improve empirical modedecomposition[J]. ICASSP 2005.
[20] P. Flandrin, G. Rilling P. Goncalves. Empirical Mode Decomposition as a filter bank[J]. IEEE Sig, Proc. Lett. 2004, 11(2): 112~114.
[21] Ryan Deering,James F. Kaiser. The use of a masking signal to improve empirical mode decomposition[J]. IEEE(2005): 485~488.
[22] Yangbo Chen, Maria Q. Feng. A technique to improve the empirical mode decomposition in the Hilbert-Huang transforms[J]. EARTHQUAKE ENGINEER AND VIBRATION,2003.7,2(1):75-85
[23] Zhao Jinping. Improvement the mirror extending in empirical mode decomposition method and the technology for eliminating frequency mixing[J]. HIGN TECHNOLOGY LETTERS. 2002.9, 8(3): 40~47.
[24] Paul A. Hwang, Norden E. Huang, David W. Wang. A note on analyzing nonlinear and non-stationary ocean wave data[J]. Applied Ocean Research, 2003, 25: 187~193.
[25]邓拥军,王伟. EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3):257-260
[26]钟佑明,秦树人. Hilbert-Huang变换中的理论研究[J].振动与冲击,2002,21(4):14-17
[27]黄大吉,赵进平.希尔伯特—黄变换的端点延拓[J].海洋学报,2003,25(1):1-11
[28]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京:科学出版社,2006.
[29]胡劲松,杨世锡.基于HHT的旋转机械故障诊断方法研究[J].动力工程,2004,24(6):845-851
[30]胡劲松,杨世锡.基于HHT的转子横向裂纹故障诊断[J].动力工程,2004,24(2):218-221
[31]陈忠,郑时雄.基于经验模式分解(EMD)的齿轮箱齿轮故障诊断技术研究[J].振动工程学报. 2003,16(2):229-232
[32]赵犁丰,王振芬.基于经验模式分解的希尔伯特变换包络提取在机械故障诊断中的应用[J].青岛海洋大学学报,2002,32(6):965-970
[33]盖强,张海勇,徐晓刚. Hilbert-Huang变换的自适应多分辨分析研究[J].电子学报,2005,(3):563-566
[34]高强,杜小山,范虹等.滚动轴承故障的EMD诊断方法研究[J].振动工程学报.2007,20(1):15-18
[35]魏兰英、李明、顾学康等.Hilbert-Huang变换在谱分析中的应用.电脑知识与技术,2007, 2(7):142.
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