基于双树复小波变换的轴承故障诊断研究

中国机械工程 ›› 2011, Vol. 22 ›› Issue (20): 2446-2451.

基于双树复小波变换的轴承故障诊断研究

艾树峰

浙江传媒学院，杭州，310018

出版日期:2011-10-25 发布日期:2011-11-02
基金资助:
浙江省自然科学基金资助项目(Y1080040)
Zhejiang Provincial Natural Science Foundation of China（No. Y1080040）

Research on Bearing Fault Diagnosis Based on Dual-tree Complex Wavelet Transform

Ai Shufeng

Zhejiang University of Media and Communications,Hangzhou,310018

Online:2011-10-25 Published:2011-11-02
Supported by:
Zhejiang Provincial Natural Science Foundation of China（No. Y1080040）

摘要/Abstract

摘要：

提出了一种基于双树复小波变换解调技术的轴承故障诊断新方法。该方法利用双树复小波变换具有近似平移不变性、避免频率混叠和有效降噪的优点，首先对轴承故障振动信号进行双树复小波分解和重构，将振动信号分解成实部和虚部，然后计算振动信号的双树复小波幅值包络和包络谱。齿轮箱轴承故障振动实验信号的分析表明,该方法能在强噪声环境下准确提取轴承故障产生的周期性瞬态冲击信号，能有效消除频率混叠现象和强噪声的影响，能有效识别轴承内圈和外圈故障。

关键词:

故障诊断, 双树复小波变换, 轴承, 幅值解调, 信号处理

Abstract:

A novel method of bearing fault diagnosis based on demodulation technique of DTCWT.It is demonstrated that the proposed dual-tree complex wavelet transform has better shift invariance, reduced frequency aliasing effect and de-noising ability.The bearing fault vibration signals were firstly decomposed and reconstructed using dual-tree complex wavelet transform. Then the real and imaginary parts were obtained and the vibration signals were amplitude demodulated. In the end,the amplitude envelope and wavelet envelope spectrum were computed. Therefore, the characteristics of the bearing

faults can be recognized according to the wavelet envelope spectrum. The experimental results show that fault diagnosis based on dual-tree complex wavelet transform can

diagnose bearing faults effectively under strong noise conditions and reduce spectral aliasing.

Key words: fault diagnosis, dual-tree complex wavelet transform(DTCWT), bearing, amplitude demodulation, signal processing

中图分类号:

TH133

艾树峰.

基于双树复小波变换的轴承故障诊断研究

[J]. 中国机械工程, 2011, 22(20): 2446-2451.

AI Shu-Feng.

Research on Bearing Fault Diagnosis Based on Dual-tree Complex Wavelet Transform

[J]. China Mechanical Engineering, 2011, 22(20): 2446-2451.

参考文献

[1]Yang Yu,Yu Dejie,Cheng Junsheng. A Fault Diag- nosis Approach for Roller Bearing Based on IMF Envelope Spectrum and SVM [J]. Measurement, 2007,40(9):943-950.
[2]李辉[1],郑海起[2],唐力伟[2].阶次包络谱在轴承故障诊断中的应用[J].机械强度:351-355.
[3]Li C J ,Ma Jun. Wavelet Decomposition of Vibration for Detection of Bearing- localized Defects [[J].NDT^E International, 1997,30(3) : 143-149.
[4]Prabhakar S, Mohanty A R, Sekhar A S. Application of Discrete Wavelet Transform for Detection of Ball Bearing Race Fault[J].Tribology International, 2002,35(12) :793-800.
[5]Lin Jing, Qu Liangsheng. Feature Extraction Based on Morlet Wavelet and Its Application for Mechani- cal Fault Diagnosis[J]. Journal of Sound and Vibra- tion,2000,234(1) :135-148.
[6]Kingsbury N G. The Dual-tree Complex Wavelet Transform: a New Technique for Shift Invariance and Directional Filters[J]. IEEE Digital Signal Pro- cessing Workshop,1998,98(1) :2-5.
[7]Selesnick I W, Baraniuk R G, Kingsbury N G. The Dual-tree Complex Wavelet Transform [J].IEEE Signal Processing Magazine,2005,22(6) 123-151.
[8]Celik T, Ozkaramanl H, Demirel H. Facial Feature Extraction Using Complex Dual- tree Wavelet Transform[J]. Computer Vision and Image Under- standing, 2008,111 (2) : 229-246.
[9]Edward H S L,Mark R P,Michael R F,et al. Image Segmentation from Scale and Rotation Invariant Texture Features from the Double Dyadic Dual- tree Complex Wavelet Transform [J].Image and Vision Computing,2011,29(1) 15-28.
[10]Priyaa K J, Rajeshb R S. Local Fusion of Complex Dual-tree Wavelet Coefficients Based Face Recog- nition for Single Sample Problem[J].Procedia Computer Science, 2010,2 ( 1 ) : 94-100.
[11]王娜[1],郑德忠[1],刘永红[1].双树复小波包变换语音增强新算法[J].传感技术学报,2009,22(7):983-987. 2
[12]Wang Yanxue, He Zhengjia,Zi Yanyang. Enhance- ment of Signal Denoising and Multiple Fault Signa- tures Detecting in Rotating Machinery Using Dual -tree Complex Wavelet Transform[J]. Mechanical Systems and Signal Processing, 2010, 24 (1) : 119-137.
[13]Li Hui, Zhang Yuping, Zheng Haiqi. Hilbert- Huang Transform and Marginal Spectrum for De- tection and Diagnosis of Localized Defects in Roller Bearings [J]. Journal of Mechanical Science and Technology, 2009,23 (2) :291-301.
[14]Li Hui,Zhang Yuping,Zheng Haiqi. Application of Hermitian Wavelet to Crack Fault Detection in Gearbox[J]. Mechanical Systems and Signal Pro- cessing, 2011,25(4) :1353-1363.

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