Abstract：In order to realize the effective extraction of rolling bearing fault periodic impulse features and solve the problem of Fourier spectral segmentation in empirical wavelet transform, an improved adaptive parameterless empirical wavelet transform method is proposed. First, adaptive segmentation of the Fourier spectrum is performed by adaptive parameterless empirical wavelet transform. Then, the spectral boundaries are combined by using the kurtosis index, and the filter banks are reconstructed to decompose the signal; finally, the component corresponding to the largest kurtosis value is selected to extract fault features by the Hilbert transform. The simulation and engineering application verify the effectiveness of the proposed method. The analysis results show that the performance of the proposed method is better than ensemble empirical model decomposition and the classical empirical wavelet transform.
［1］陈雪峰, 郭艳婕. 风电装备振动监测与诊断［M］. 北京: 科学出版社, 2016: 191.
［2］Sun R B, Yang Z B, Chen X F, et al. Gear fault diagnosis based on the structured sparsity time-frequency analysis［J］. Mechanical Systems and Signal Processing, 2018, 102: 346-363.
［3］何群, 王红, 江国乾, 等. 基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究［J］. 计量学报, 2018, 39(1): 89-93.
He Q, Wang H, Jiang G Q, et al. Research of wind turbine main bearing condition monitoring based on correlation PCA and ELM［J］. Acta Metrologica Sinica, 2018, 39(1): 89-93.
［4］谢平, 王一凡, 江国乾, 等. 基于样本熵的风力发电机早期故障检测［J］. 计量学报, 2017, 38(5): 626-630.
Xie P, Wang Y F, Jiang G Q, et al. Incipient fault detection of generators in wind turbines based on sample entropy［J］. Acta Metrologica Sinica, 2017, 38(5): 626-630.
［5］Gilles J. Empirical wavelet transform［J］. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
［6］李志农, 朱明, 褚福磊, 等. 基于经验小波变换的机械故障诊断方法研究［J］. 仪器仪表学报, 2014, 35(11): 2423-2432.
Li Z N, Zhu M, Chu F L, et al. Mechanical fault diagnosis method based on empirical wavelet transform［J］. Chinese Journal of Scientific Instrument, 2014, 35(11): 2423-2432.
［7］Merainani B, Benazzouz D, Bouamama B O, et al. Early fault diagnosis of gearbox using Empirical Wavelet Transform and Hilbert Transform［C］// Control and Automation. IEEE, 2016: 1367-1372.
［8］王友仁, 陈伟, 孙灿飞, 等. 基于能量聚集度经验小波变换的齿轮箱早期微弱故障诊断［J］. 中国机械工程, 2017, 28(12): 1484-1490.
Wang Y R, Chen W, Sun C F, et al. Early weak fault diagnosis of gearboxes based on energy aggregation and EWT［J］. China Mechanical Engineering, 2017, 28(12): 1484-1490.
［9］祝文颖, 冯志鹏. 基于改进经验小波变换的行星齿轮箱故障诊断［J］. 仪器仪表学报, 2016, 37(10):2193-2201.
Zhu W Y, Feng Z P. Fault diagnosis of planetary gearbox based on improved empirical wavelet transform ［J］. Chinese Journal of Scientific Instrument, 2016, 37(10): 2193-2201.
［10］郑近德, 潘海洋, 潘紫微, 等. 自适应无参经验小波变换及其在转子故障诊断中的应用［J］. 中国机械工程, 2016, 27(16): 2218-2224.
Zheng J D, Pan H Y, Pan Z W, et al. Adaptive parameterless empirical wavelet transform and its applications to fault diagnosis of rotor system［J］. China Mechanical Engineering, 2016, 27(16): 2218-2224.
［11］张金凤,李继猛,杨莹, 等. 基于改进耦合增强随机共振的滚动轴承故障诊断［J］. 计量学报, 2019, 40(3): 385-391.
Zhang J F, Li J M, Yang Y, et al. Rolling Bearing Fault Diagnosis Based on Improved
Coupling-enhanced Stochastic Resonance［J］. Acta Metrologica Sinica, 2019, 40(3): 385-391.
［12］Gilles J, 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 & Information Processing, 2014, 12(6): 1450044_1-17.
［13］Otsu N. A threshold selection method from gray-level histograms［J］. IEEE Transaction on Systems, Man and Cybernetics, 1979, 9(1): 62-66.
2020, Vol. 41 
