华电技术 ›› 2020, Vol. 42 ›› Issue (1): 54-57.

• 研究与开发 • 上一篇    下一篇

自适应调频模式分解在轴承故障诊断中的应用

  

  1. 福建华电可门发电有限公司,福州〓350500
  • 出版日期:2020-01-25 发布日期:2020-03-20

Application of ACMD in fault diagnosis for bearings

  1. Fujian Huadian Kemen Power Generation Company Limited,Fuzhou 350500, China
  • Online:2020-01-25 Published:2020-03-20

摘要: 轴承故障诊断中的振动信号易受噪声干扰,具有多分量、非平稳的特性。为寻找更优的轴承故障诊断方法,研究了自适应调频模式分解法(ACMD)和其应用。该方法将轴承故障信号自适应分解为多个分量,然后选择峭度最大的分量进行希尔伯特变换并计算包络谱,最后通过分析包络谱中的轴承故障特征频率实现故障的诊断。该方法能够有效提取滚动轴承故障信号的特征频率,有良好的应用前景。

关键词: 滚动轴承, 故障诊断, 自适应调频模式分解, 峭度, 包络谱

Abstract: Vibration signal which is of multiple components and nonstationary, is susceptible to noise interference in bearing fault diagnosis. In order to find a better method for bearing fault diagnosis, adaptive chirp mode decomposition (ACMD) and its application are studied.Firstly, the fault signal of bearings was decomposed adaptively into several components by ACMD. Secondly, the envelope spectrum of the component with biggest kurtosis was obtained by Hilbert transform and calculated. Finally, the fault was diagnosed by figuring out the characteristic frequency in the envelope spectrum. The method can extract the characteristic frequency of fault signals effectively and be of a good application prospect.

Key words: rolling bearing, fault diagnosis, adaptive chirp mode decomposition, kurtosis, envelope spectrum