改进的自适应经验傅里叶分解方法及其在滚动轴承故障诊断中的应用

曹仕骏1,2,郑近德1,2,潘海洋1,2,童靳于1,2,刘庆运1,2

振动与冲击 ›› 2022, Vol. 41 ›› Issue (15) : 287-299.

PDF(5544 KB)
PDF(5544 KB)
振动与冲击 ›› 2022, Vol. 41 ›› Issue (15) : 287-299.
论文

改进的自适应经验傅里叶分解方法及其在滚动轴承故障诊断中的应用

  • 曹仕骏1,2,郑近德1,2,潘海洋1,2,童靳于1,2,刘庆运1,2
作者信息 +

Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method

  • CAO Shijun1,2, ZHENG Jinde1,2, PAN Haiyang1,2, TONG Jinyu1,2, LIU Qingyun1,2
Author information +
文章历史 +

摘要

自适应经验傅里叶分解(Adaptive Empirical Fourier Decomposition,简称AEFD)是最近提出的非平稳信号分解方法,为了解决AEFD的分割边界集设置问题,提出了基于频谱包络检测的改进自适应经验傅里叶分解(Enhanced AEFD,简称EAEFD)方法,该方法以快速傅里叶变换为基础,以包络熵值最小选择最优的分解模态数目,采用极大值包络技术对傅里叶频谱分割,得到一个合理的分割边界,最后采用逆快速傅里叶变换对每个区间信号进行重构。EAEFD能够自适应地将一个复杂信号自适应地分解为若干个瞬时频率具有物理意义的单分量信号之和,通过仿真信号和滚动轴承信号分析,将EAEFD方法与EWT,EMD,LCD和AEFD等方法进行了对比,结果表明EAEFD方法不仅仅能够有效地诊断出故障特征,而且诊断的精度更高。
关键词:自适应经验傅里叶分解;包络熵;经验模态分解;滚动轴承;故障诊断

Abstract

Adaptive empirical Fourier decomposition (AEFD) is a recently proposed method for non-stationary signal decomposition. An enhanced adaptive empirical Fourier decomposition (EAEFD) method is proposed based on spectrum envelope detection to solve the problem of dividing boundary set setting of AEFD, which is based on the fast Fourier transform, and the optimal number of decomposition modes is selected by minimizing envelope entropy. The maximum envelopment technology is used to segment the Fourier spectrum, and a reasonable segmentation boundary is obtained. Finally, the inverse fast Fourier transform is used to reconstruct the signals in each interval. EAEFD can adaptively decompose a complex signal into the sum of several single component signals whose instantaneous frequencies have physical significance. The EAEFD method is compared with the EWT, EMD, LCD and AEFD methods by analyzing the simulation and rolling bearing vibration signals. The results show that EAEFD can diagnose fault features effectively and has higher accuracy than the compared methods.
Key words: Adaptive Empirical Fourier Decomposition; Envelope entropy; empirical mode decomposition; rolling bearing; fault diagnosis

关键词

自适应经验傅里叶分解 / 包络熵 / 经验模态分解 / 滚动轴承 / 故障诊断

Key words

Adaptive Empirical Fourier Decomposition / Envelope entropy / empirical mode decomposition / rolling bearing / fault diagnosis

引用本文

导出引用
曹仕骏1,2,郑近德1,2,潘海洋1,2,童靳于1,2,刘庆运1,2. 改进的自适应经验傅里叶分解方法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2022, 41(15): 287-299
CAO Shijun1,2, ZHENG Jinde1,2, PAN Haiyang1,2, TONG Jinyu1,2, LIU Qingyun1,2. Enhanced adaptive empirical Fourier decomposition based rolling bearing fault diagnosis method[J]. Journal of Vibration and Shock, 2022, 41(15): 287-299

参考文献

[1] HUANG HaiRun, LI Ke, SU WenSheng et al. An improved empirical wavelet transform method for rolling bearing fault diagnosis[J].Science China Technological Sciences, 2020, 63(11): 2231-2240.
[2] 梅宏斌.滚动轴承振动监测与诊断理论.方法.系统[M].北京:机械工业出版社,1996.
MEI Hongbin.Vibration Monitoring and Diagnosis Theory of Rolling Bearing.Method. System [M]. Beijing: China Machine Press,1996
[3] Howard C. Choe, Yulun Wan, Andrew K. Chan. Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features[J].Proceedings of SPIE - The International Society for Optical Engineering, 1997(3078):480-496.
[4] 吴斌,冯长建,罗跃纲,等.滚动轴承故障的振动信号诊断方法[J].机械设计与制造, 2009(11):178-179.
WU Bin, FENG Chang-jian, LUO Yue-gang, et al.A hybrid diagnosis method based on vibration signals for rolling bearing fault[J].Machinery Design & Manufacture, 2009(11):178-179.
[5] FENG Zhipeng, LIANG Ming, CHU Fulei. Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples [J].Mechanical Systems and Signal Processing, 2013, 38(1): 165-205.
[6] Cheng J S, Yu D J, Yang Y. Time-energy density analysis based on wavelet transform [J]. NDT&E International, 2005 ,38 (7) :569-572.
[7] 尹爱军,李海珠,李江,等. Wigner-Ville分布复小波相似性评价及应用[J]. 振动.测试与诊断,2020,40(01):7-11.
Yin Aijun, Li Haizhu, Li Jiang, et al.Evaluation and Application of Wigner-Ville Distribution Complex Wavelet Similarity[J]. Journal of Vibration, Measurement & Diagnosis,2020,40(01):7-11.
[8] Chui Charles K.,Jiang Qingtang,Li Lin, et al. Analysis of an adaptive short-time Fourier transform-based multicomponent signal separation method derived from linear chirp local approximation[J]. Journal of Computational and Applied Mathematics,2021,396.
[9] 唐向宏,李齐良.时频分析与小波变换[M].北京:科学出版社,2008.
TANG Xianghong, LI Qiliang. Time-frequency Analysis and Wavelet Transform [M]. Beijing: Science Press,2008
[10] HUANG N E, SHEN Z, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society of London, 1998,454 (1):903 -995.
[11] Huang N E, Shen Z, Long S R. A new view of nonlinear water waves: The Hilbert spectrum [J]. Annual Review of  Fluid Mechanics, 1999,31:417 -457.
[12] 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,2007.
YU Dejie, CHENG Junsheng, YANG Yu.Research on Fault Diagnosis Methods for Rotating Machinery Based on Hilbert-Huang Transform[M].Beijing: Science Press,2007
[13] Huang N E,Wu Z. A review on Hilbert-Huang transform:Method and its applications to geophysical studies. Advances in Adaptive Data Analysis 2009.1:1-23.
[14] 程军圣,郑近德,杨宇.一种新的非平稳信号分析方法——局部特征尺度分解法[J]. 振动工程学报,2012,25(02):215-220.
Cheng Junsheng, Zheng Jande, Yang Yu. A New Method For Non-stationary Signal Analysis: Local Characteristic-scale Decomposition[J].Journal of Vibration Engineering, 2012,25(02):215-220.
[15] 杨宇,曾鸣,程军圣.一种新的时频分析方法——局部特征尺度分解[J]. 湖南大学学报(自然科学版),2012,39(06):35-39.
YANG Yu,ZENG Ming,CHENG Jun-sheng.A New Time-frequency Analysis Method-the Local Characteristic-scale Decomposition[J].Journal of Hunan University(Natural Sciences),2012,39(06):35-39.
[16] 杨宇,曾鸣,程军圣.局部特征尺度分解方法及其分解能力研究[J].振动工程学报,2012,25(05):602-609.
YANG Yu,ZENG Ming,CHENG Jun-sheng.Study On Local Characteristic-Scale Decomposition Method and Its Capacity.[J].Journal of Vibration Engineering, 2012, 25(05):602-609.
[17] Gilles J. Empirical Wavelet Transform. IEEE Transactions on Signal Processing [J]. 2013, 61(16) :3999-4010.
[18] Gilles J. Tran G, Osher S. 2D Empirical Trans forms: Wavelets. Ridgelets and Curvelets Revisirted[J]. SIAM Journal on Imaging Sciences, 2014.7(1):157-186.
[19] 李从志.基于经验小波变换与散布熵的滚动轴承故障诊断方法研究[D].安徽工业大学,2019.
LI Congzhi.Research on Empirical Wavelet Transform and Dispersion Entropy based Fault Diagnosis of Rolling Bearing[D].Anhui University of Technology,2019.
[20] 郑近德,潘海洋,程军圣,等.基于自适应经验傅里叶分解的机械故障诊断方法[J].机械工程学报,2020,56(09):125-136.
ZHENG Jinde,PAN Haiyang, CHENG Junsheng,et all.Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method[J].JOURNAL OF MECHANICAL ENGINEERING,2020,56(09):125-136.
[21] 唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报,2015,49(05):73-81.
TANG Guii,WANG Xiaolong.Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing[J].JOURNAL OF XI'AN JIAOTONG UNIVERSITY,2015,49(05):73-81.
[22] 程军圣,张亢,杨宇,等.局部均值分解与经验模式分解的对比研究[J].振动与冲击,2009,28(05):13-16.
CHENG Junsheng, ZHANG Kang, YANG Yu.Comparative Study of Local Mean Decomposition and Empirical Mode Decomposition[J].JURNAL OF VBRAT ON AND SHOCK,2009,28(05):13-16.
[23] Lu Siliang, He Qingbo, Wang Jun. A review of stochastic resonance in rotating machine fault detection[J]. Mechanical systems and signal processing, 2018, 116: 230-260.

PDF(5544 KB)

Accesses

Citation

Detail

段落导航
相关文章

/