基于形态分量分析和包络谱的轴承故障诊断

摘要/Abstract

摘要：

滚动轴承出现局部损伤时,其振动信号往往由包含轴承自身振动的谐振分量、包含轴承故障信息的冲击分量及随机噪声分量构成。提出了基于形态分量分析和包络谱的滚动轴承故障诊断方法。该方法根据轴承振动信号中各组成成分的形态差异,利用改进的形态分量分析对滚动轴承故障振动信号中的谐振分量、冲击分量和噪声分量进行分离,然后对冲击分量进行Hilbert包络解调分析,根据包络谱诊断滚动轴承故障。算法仿真和应用实例表明,该方法能有效提取滚动轴承故障特征。

关键词: 形态分量分析, 半软阈值, 包络谱, 滚动轴承, 故障诊断

Abstract:

When a rolling bearing was locally damaged, its vibration signals were often composed of harmonic components with system characteristics of the rolling bearing, impulse components with fault information and random noise. A new method for the fault diagnosis of rolling bearings was proposed based on the MCA and envelope spectrum. The harmonic components, impulse components and random noise components were separated from the vibration signals of a fault rolling bearing by using the improved MCA, the impulse components were then analyzed by using the Hilbert envelope demodulation analysis, and the fault diagnosis of rolling bearing was carried out according to the envelope spectrum. Simulation and application examples show that the proposed method is effective in extracting the fault characteristics from the vibration signals of local damaged rolling bearings.

Key words: morphological component analysis(MCA), semisoft shrinkage, envelope spectrum, rolling bearing, fault diagnosis

中图分类号:

陈向民, 于德介, 李蓉. 基于形态分量分析和包络谱的轴承故障诊断[J]. 中国机械工程, 2014, 25(8): 1047-1053.

Chen Xiangmin, Yu Dejie, Li Rong. Fault Diagnosis of Rolling Bearings Based on Morphological Component Analysis and Envelope Spectrum[J]. China Mechanical Engineering, 2014, 25(8): 1047-1053.

参考文献

[1]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京:机械工业出版社,2005.
[2]王平,廖明夫.滚动轴承故障诊断中的自适应共振解调技术[J].航空动力学报,2005,20(4):606-612.
Wang Ping,Liao Mingfu.Adaptive Demodulated Resonance Technique for the Rolling Bearing Fault Diagnosis[J].Journal of Aerospace Power,2005,20(4):606-612.
[3]邓四二,王勇,王恒迪.基于IHT的共振解调技术的滚动轴承故障诊断方法[J].航空动力学报,2012,27(1):69-74.
Deng Sier,Wang Yong,Wang Hengdi.Resonance Demodulation Technique Based on IHT for Rolling Bearings Fault Diagnosis[J]. Journal of Aerospace Power,2012,27(1):69-74.
[4]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.
[5]Nikolaou N G,Antoniadis I A. Rolling Element Bearing Fault Diagnosis Using Wavelet Packets[J].NDT&E International,2002,35(3):197-205.
[6]史东峰,鲍明,屈梁生.小波包络分析在滚动轴承诊断中的应用[J].中国机械工程,2000,11(12):1382-1385.
Shi Dongfeng,Bao Ming,Qu Liangsheng.Application of Wavelet Envelope Analysis to Rolling Bearing Diagnosis[J].China Mechanical Engineering,2000,11(12):1382-1385.
[7]艾树峰.基于多尺度Hernitian小波包络谱的轴承故障诊断[J].中国机械工程,2012,23(1):34-38.
Ai Shufeng.Bearing Fault Diagnosis Based on Multi-scale Hermitian Wavelet Envelope Spectrum[J].China Mechanical Engineering,2012,23(1):34-38.
[8]杨宇,于德介,程军圣.基于经验模态分解包络谱的滚动轴承故障诊断方法[J].中国机械工程,2004,15(16):1469-1471.
Yang Yu,Yu Dejie,Cheng Junsheng.A Fault Diagnosis Approach for Roller Bearings Based on EMD and Envelope Spectrum[J]. China Mechanical Engineering,2004,15(16):1469-1471.
[9]高强,杜小山,范虹,等.滚动轴承故障的EMD诊断方法研究[J].振动工程学报,2007,20(1):15-18.
Gao Qiang,Du Xiaoshan,Fan Hong,et al.An Empirical Mode Decomposition Based Method for Rolling Bearing Fault Diagnosis[J]. Journal of Vibration Engineering,2007,20(1):15-18.
[10]李辉,郑海起,杨绍普.基于EMD和Teager能量算子的轴承故障诊断研究[J].振动与冲击,2008,27(10):15-17,22.
Li Hui,Zheng Haiqi,Yang Shaopu.Bearing Fault Diagnosis Based on EMD and Teager Kaiser Energy Operator[J].Journal of Vibration and Shock,2008,27(10):15-17,22.
[11]程军圣,张亢,杨宇.局部均值分解方法及其在滚动轴承故障诊断中的应用[J].中国机械工程,2009,20(22):2711-2717.
Cheng Junsheng,Zhang Kang,Yang Yu.Local Mean Decomposition Method and Its Application to Roller Bearing Fault Diagnosis[J].China Mechanical Engineering,2009,20(22):2711-2717.
[12]陈亚农,郜普刚,何田,等.局部均值分解在滚动轴承故障综合诊断中的应用[J].振动与冲击,2012,31(3):73-78.
Chen Yanong,Gao Pugang,He Tian,et al.Roller Bearing Comprehensive Fault Diagnosis Based on LMD[J].Journal of Vibration and Shock,2012,31(3):73-78.
[13]Starck J L,Moudden Y,Robin J.Morphological Component Analysis[J].Proceedings of SPIE,2005,59(14):1-15.
[14]李映,张艳宁,许星.基于信号稀疏表示的形态成分分析:进展和展望[J]. 电子学报,2009,37(1):146-152.
Li Ying,Zhang Yanning,Xu Xing.Advances and Perspective on Morphological Component Analysis Based on Sparse Representation[J].Acta Electronica Sinica,2009,37(1):146-152.
[15]Starck J L,Elad M,Donoho D.Redundant Multiscale Transforms and Their Application for Morphological Component Separation[J].Advances in Imaging and Electron Physics,2004,132(82):287-384.
[16]Elad M,Starck J L,Querre P.Simultaneous Cartoon and Texture Image Inpainting Using Morphological Component Analysis(MCA)[J]. Journal on Applied and Computational Harmonic Analysis,2005,19(3):340-358.
[17]Yong X Y,Ward R K,Birch G E.Generalized Morphological Component Analysis for EEG Source Separation and Artifact Removal[C]//Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering. Antalya, Turkey, 2009:343-347.
[18]杨杰,郑海起,关贞珍,等.基于形态成分分析的轴承复合故障诊断[J].轴承,2011(8):38-42.
Yang Jie,Zheng Haiqi,Guan Zhenzhen,et al.Compound Fault Diagnosis for Bearings Based on Morphological Component Analysis[J].Bearing,2011(8):38-42.
[19]李辉,郑海起,唐力伟.基于改进形态分量分析的齿轮箱轴承多故障诊断研究[J].振动与冲击,2012,31(12):135-140.
Li Hui,Zheng Haiqi,Tang Liwei.Bearing Multi-fault Diagnosis Based on Improved Morphological Component Analysis[J]. Journal of Vibration and Shock,2012,31(12):135-140.
[20]Chen S S,Donoho D L,Saunders M A.Atomic Decomposition by Basis Pursuit[J].SIAM Journal on Scientific Computing, 1998, 20(1):33-61.
[21]Mallet S G,Zhang Z F.Matching Pursuit with Time-frequency Dictionaries[J].IEEE Transaction on Signal Processing, 1993, 41(12):3397-3415.
[22]Bobin J, Starck J L, Fadili J. Morphological Component Analysis:an Adaptive Thresholding Strategy[J].IEEE Transaction on Image Processing,2007,16(11):2675-2681.
[23]Gao Hongye,Bruce A G.Waveshrink and Semisoft Shrinkage[J].StaSci Research Report,1995,39:5-8.
[24]Liang Ming,Bozchalooi I S.An Energy Operator Approach to Joint Application of Amplitude and Frequency-demodulations for Bearing Fault Detection[J].Mechanical Systems and Signal Processing,2010,24(5):1473-1494.