Fault feature extraction of a wind turbine gearbox using adaptive parameterless empirical wavelet transform
DING Xian1,XU Jin1,TENG Wei2,WANG Wei2
1.Luneng Group Co., Ltd., Beijing 100020, China;
2. Key Laboratory of Power Station Energy Transfer Conversion and System, Ministry of Education, North China Electric Power University, Beijing 102206,China
Wind turbines operate as an equipment cluster, bringing massive vibration signals due to their complex structures and multiply vibration measures.Only analysing the vibration signals to detect fault by human is challenging.In this paper, a fault feature extraction method for wind turbine gearboxes was proposed on the basis of the parameterless empirical wavelet transform.The scale space method and empirical law were utilized to automatically split the Fourier spectrum of the vibration signal, and different frequency bands were obtained.A series of empirical wavelet filters were designed based on the split frequency bands to decompose the signal into multiply empirical modes.The metric of margin factor was adopted to sort the empirical modes, and the empirical mode with maximum margin factor was recognized as the most sensitive one to fault.The proposed method is adaptive without any presented parameters.The fault signals from an experimental platform and a real wind turbine gearbox verified the proposed method.
[1] http://gwec.net/publications/global-wind-report-2/
[2] 李 恒,张 氢,秦仙蓉,孙远韬. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法 [J]. 振动与冲击,2018, 37(19):124-131.
LI Heng, ZHANG Qin, QIN Xian-rong, SUN Yuan-tao. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network [J]. Journal of Vibration Engineering, 2018, 37(19): 124-131.
[3] 秦毅,秦树人,毛永芳. 基于小波脊线的解调方法及其在旋转机械故障诊断中的应用 [J]. 机械工程学报,2009,45(2):231-237.
QIN Yi, QIN Shu-ren, MAO Yong-fang. Demodulation approach based on wavelet ridge and its application in fault diagnosis of rotating machinery [J]. Journal of Mechanical Engineering, 2009, 45(2): 231-237.
[4] Antoni J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions [J]. Journal of Sound and Vibration, 2007, 304(3-5): 497-529.
[5] Antoni J, Randall R B. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines [J], Mechanical Systems and Signal Processing, 2006, 20(2): 308-331.
[6] Li G, Zhao Q. Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection [J], Journal of Sound and Vibration, 2017, (390): 218-231.
[7] 李康强,冯志鹏. 基于EMD和能量算子的模态参数识别在行星齿轮箱中的应用 [J]. 振动与冲击,2018, 37(8):1-8.
LI Kang-qiang, FENG Zhi-peng. Modal parameter identification based on empirical mode decomposition and energy operator for planetary gearboxes [J], Journal of Vibration and Shock, 2018, 37(8): 1-8. [8] Hu A J, Yan X A, Xiang L. A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension [J]. Renewable Energy, 2015, 83: 767-778.
[9] Gilles J. Empirical Wavelet Transform [J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[10] 黄南天,张书鑫,蔡国伟,等. 采用EWT和OCSVM的高压断路器机械故障诊断 [J]. 仪器仪表学报,2015,(12):2773-2781.
HUANG Nan-tian, ZHANG Shu-xin, CAI Guo-wei, et al. Mechanical fault diagnosis of high voltage circuit breakers utilizing empirical wavelet transform and one-class support vector machine [J]. Chinese Journal of Scientific Instrument, 2015, (12): 2773-2781.
[11] Gilles J M, Heal K. A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation [J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014, 12(6): 1-17.
[12] Rudemo M. Empirical choice of histograms and kernel density estimators [J]. Scandinavian Journal of Statistics, 1982, 9(2): 65-78. [13] Daubechies I, Ten lectures on wavelets, ser [C]. CBMS-NSF Regional Conference Series in Applied Mathematics, 1992, Philadelphia, PA, USA: SIAM.