EEMD能量熵分析及在齿轮箱故障诊断中的应用
Application of EEMD Energy Entropy Method to Fault Diagnosis of Gearbox
DOI: 10.12677/MET.2012.14012, PDF, HTML,  被引量 下载: 3,971  浏览: 13,235  国家自然科学基金支持
作者: 石智云*, 贾民平*：东南大学机械工程学院，南京
关键词: 齿轮箱；EEMD；能量熵；幅值；分解次数；Gearbox; EEMD; Energy Entropy; Amplitude; Ensemble Number
摘要: 针对齿轮箱振动信号的非平稳、非线性等特点，提出一种基于总体平均经验模态分解EEMD的能量熵信号分析及故障诊断方法。该方法利用EEMD方法能够有效抑制模式混叠现象的特点，先对原始振动信号进行EEMD分解，得到各阶本征模态函数(IMFs)，然后求得将各阶本征模态函数的能量及其熵。指出能量熵的值能够反映系统的工作状态和故障类型。通过对白噪声幅值及分解次数对齿轮箱振动加速度信号分析对比，得出最优化选择方案。
Abstract: For the non-stationary and non-liner characteristics of gearbox vibration signal, ensemble empirical mode decomposition (EEMD) method based energy entropy is proposed for signal analysis and fault diagnosis of gearbox. This method utilizes the advantage of EEMD which can effectively restrain model mixing. Firstly, EEMD method is used to decompose the original signal to get intrinsic mode functions (IMFs). Then, energy of each IMF is calculated. Finally, the energy entropy IMFs is obtained, since energy entropy can reflect the system’s working condition and fault type. The number of ensemble and the amplitude of the added white noise are two parameters need to be set. Different parameters are analyzed in gearbox vibration signals with comparison, aiming at a best choice.
文章引用：石智云, 贾民平. EEMD能量熵分析及在齿轮箱故障诊断中的应用[J]. 机械工程与技术, 2012, 1(4): 61-67. http://dx.doi.org/10.12677/MET.2012.14012

参考文献

[1] 陈汉新, 王庆军, 陈绪兵等. 基于解调振动信号特征提取齿轮箱的故障诊断[J]. 武汉工程大学学报, 2010, 32(9): 67-77.
[2] B. Liu, S. Riemenschneuder and Y. Xu. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mechanical Systems and Signal Processing, 2006, 20(3): 718-734.
[3] 张超, 陈建军, 郭迅. 基于EMD能量熵和支持向量机的齿轮故障诊断方法[J]. 振动与冲击, 2010, 29(10): 216-219.
[4] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and non- stationary time series analysis. Proceedings of the Royal Society A, 1998, 454(1971): 903-995.
[5] N. E. Huang, Z. H. Wu. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[6] Y. G. Lei, Z. J. He and Y. Zi. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical System and Signal Processing, 2009, 23(4): 1327-1338.
[7] 高青青, 贾民平. 基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J]. 东南大学报, 2011, 41(5): 998-1001.
[8] Y. Yang, D. J. Yu and J. S. Cheng. A rolling fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration, 2006, 294(1): 269-277.
[9] 苏中元, 贾民平. 周期平稳信号盲源分离算法及其应用[J]. 机械工程学报, 2007, 43(10): 144-149.
[10] 张超, 陈建军. EEMD方法和EMD方法抗模态混叠对比研究[J]. 振动与冲击, 2010, 29(S): 87-90.
[11] Z. Wu and N. E. Huang. A study of the char-acteristics of white noise using the empirical mode decomposition method. Proceedings of the Royal Society A: London, 2004, 460(2046): 1597- 1611.

为你推荐