Complementary ensemble adaptive sparsest narrow-band decomposition and its application

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (20) : 31-37.

CHEN Junhang1,2,PENG Yanfeng1,2,LI Xuejun1,2,HAN Qingkai3,LI Hongguang1,4

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Abstract

Adaptive sparsest narrow-band decomposition (ASNBD) is the most sparse solution for searching signals in the over-complete dictionary library containing intrinsic mode functions (IMF), which transforms the signal Decomposition into an optimization problem, but the calculation accuracy still needs to be improved in the case of strong noise interference.Therefore, in combination with the algorithm of thecomplementary ensemble empirical mode decomposition (CEEMD), a new method of the complementary ensemble adaptive sparsest narrow-band decomposition (CE-ASNBD) was obtained.In this method, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters.The analysis results of simulation and experimental data show that this method is superior to CEEMD and ASNBD in inhibiting mode confusion, endpoint effect, performance, improving component orthogonality and accuracy, and can be effectively used in fault diagnosis of rolling bearing.

Key words

fault diagnosis / rolling bearing / adaptive sparsest narrow-band decomposition / complementary ensemble empirical mode decomposition / local narrow-band singal

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CHEN Junhang1,2,PENG Yanfeng1,2,LI Xuejun1,2,HAN Qingkai3,LI Hongguang1,4. Complementary ensemble adaptive sparsest narrow-band decomposition and its application[J]. Journal of Vibration and Shock, 2019, 38(20): 31-37

References

[1] 尹忠科, 解梅, 王建英. 基于稀疏分解的图像去噪[J]. 电子科技大学学报, 2006, 35(6):876-878.
Yin Z K，Xie M，Wang J Y，Image denoising based on its sparse decomposition Journal of University of Electronic Science and Technology of China. 2006, 35(6): 876-878.
[2] Liu D H, Shi G M, Zhou J S. New method for signal sparse decomposition over a redundant dictionary[J]. Journal of Xidian University, 2008.
[3] 彭延峰, 程军圣, 杨宇. ACROA优化的自适应最稀疏窄带分解方法[J]. 振动工程学报, 2016, 29(6): 1127-1133.
Peng Y, Cheng J, Yang Y, Adaptive sparsest narrow band method optimized by ACROA. Journal of Vibration Engineering, 2016, 29(6): 1127-1133.
[4] 史丽丽. 基于稀疏分解的信号去噪方法研究[D]. 哈尔滨工业大学, 2013.
Shi Lili, Research on denoising methods of signals based on sparse decomposition. Harbin Institute of Technology, 2013.
[5] 宋昌浩, 纪国宜. 遗传算法优化稀疏分解的齿轮箱故障诊断研究[J]. 噪声与振动控制, 2017, 37(5): 175-179.
Song Changhao, JI Guoyi, Research on fault diagnosis of gearboxes based on genetic algorithm optimized sparse decomposition. Noise and vibration control, 2017, 37(5):175-179.
[6] Wu Z, Huang N E. Ensemble empirical mode decomposition: A noise assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1: 1-41．
[7] Yeh J R，Shieh J S. Complementary ensemble empirical mode decomposition：A noise enhanced data analysis method[J]，Advances in Adaptive Data Analysis，2010，2(2)：135-156.
[8] 罗洁思，于德介，彭富强. 基于多尺度线调频基信号稀疏分解的信号分离和瞬时频率估计[J]. 电子学报，2010，38(10)：2224-2228.
LUO Jiesi, YU Dejie, PENG Fuqiang, Signal separation and instantaneous frequency estimation based on multi-scale chirplet sparse signal decomposition, Acta electronica sinica, 2010，38(10)：2224-2228.
[9] Frei M G，Osorio I. Intrinsic time-scale decomposition：Time-frequency-energy analysis and real-time filtering of non-stationary signals[J]，Proceedings of the Royal Society A，2007，463(2708)：321-342.
[10] Peng Y, Cheng J, Yang Y, et al. Adaptive sparsest narrow-band decomposition method and its applications to rotor fault diagnosis[J]. Measurement, 2016, 91:451-459.
[11] 苏文胜, 王奉涛, 张志新,等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击, 2010, 29(3):18-21.
Su Wensheng, Wang Fengtao Zhang Zhixing, Application of EMD noise reduction and spectral kurtosis in early fault diagnosis of rolling bearing, Journal of Vibration and Shock, 2010, 29(3):18-21.
[12] Yang H G，Mathew J，Ma L. Fault diagnosis of rolling element bearings using basis pursuit[J]. Mechanical Systems and Signal Processing，2005，19(2)：341-356
[13] Cheng J, Peng Y, Yu Y, et al. Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2017, 85(1): 947-962.
[14] 蔡文航, 周世健, 吴金斌. CEEMD联合小波阈值去噪法及其在GPS多路径效应中的应用[J]. 江西科学, 2018(1): 73-78.
Cai Wenhang Zhou Shiqiang Wu Jinbing, Denoising method based on CEEMD and wavelet threshold and its Application in GPS Multipath Effect, Jiangxi science, 2018(1):73-78.
[15] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory，2006，52(4)：1289-1396.
[16] Yang H G，Mathew J，Ma L. Fault diagnosis of rolling element bearings using basis pursuit[J]. Mechanical Systems and Signal Processing，2005，19(2)：341-356.
[17] LUO Jiesi，YU Dejie，PENG Fuqiang. Signal separation and instantaneous frequency estimation based on multi-scale chirplet sparse signal decomposition[J]. Acta Electronica Sinica，2010，38(10)：2224-2228.
[18] 陆森林, 王龙. CEEMD-FFT在滚动轴承故障诊断中的应用[J]. 郑州大学学报(工学版), 2015, 36(1): 75-78.
Lu Senlin. Wang Long, Application of CEEMD-FFT in roller bearing fault diagnosis[J], Journal of Zhengzhou University, 2015, 36(1):75-78.
[19] Lei Y, Lin J, He Z, et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings[J]. Mechanical Systems & Signal Processing, 2011, 25(5):1738-1749.