噪声与振动控制

›› 2011, Vol. 31 ›› Issue (5): 133-136.DOI: 10.3969/j.issn.1006-1355-2011.05.031

• 6.信号处理与故障诊断 • 上一篇    下一篇

滚动轴承故障诊断的研究

陈永会1,姜 旭2,张学良1,李海虹1

  

  1. （ 1. 太原科技大学 机电学院， 太原 030024； 2. 机械工业工程机械军用改装车试验场， 北京 102100 ）
  • 收稿日期:2010-12-03 修回日期:2011-01-20 出版日期:2011-10-18 发布日期:2011-10-18
  • 通讯作者: 陈永会

Research of Rolling Bearings Fault Diagnosis

CHEN Yong-hui 1,JIANG Xu 2,ZHANG Xue-liang 1,LI Hai-hong 1   

  1. （ 1. Taiyuan University of Science and Technology, Taiyuan 030024, China; 2. MMI Proving Ground Construction Machinery and Refit Military Vehicles, Beijing 102100, China ）
  • Received:2010-12-03 Revised:2011-01-20 Online:2011-10-18 Published:2011-10-18
  • Contact: CHEN Yong-hui

摘要： 针对滚动轴承故障信号的非平稳和调制特点，使用小波分解，对包含故障信息的信号进行分解、重构。应用Hilbert变换进行解调和细化频谱分析，提取了故障特征频率，判断出滚动轴承故障模式。小波分解和Hilbert变换结合对滚动轴承局部损伤故障的检测是有效的。

关键词: 振动与波, 滚动轴承, 故障诊断, 小波分解, Hilbert变换

Abstract: For the non-stationary and modulation features of rolling bearing’s fault signals, a method based on wavelet analysis is employed. The signals including fault information are decomposed and reconstructed by wavelet analysis method. Then, demodulation and fine spectral analysis of the signals are carried out by using Hilbert transform. The characteristic frequencies of the fault signals are extracted, and the fault patterns of the rolling bearings can be recognized. It is found that the wavelet analysis and Hilbert transform are effective in identifying the local defects of rolling bearings.

Key words: vibration and wave, rolling bearing, fault diagnosis, wavelet analysis, Hilbert transform

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