Application of Wavelet Theory to Fault Diagnosis of Rolling Bearing
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    Abstract:

    This paper studies the performance of wavelet transform denoising, and the algorithm of Cyclic autocorrelation analysis. Based on theoretical analysis, it makes Cyclic Autocorrelation function analysis of the measured multiple signals based on wavelet threshold denoising. From the results of simulation, it is effective in extracting bearing fault characteristic frequency and suppress interference from other frequency components by the cyclic autocorrelation function amplitude spectrum. The simulation results demonstrate the effectiveness of the method.

    Reference
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    2 盛兆顺,尹琦玲.设备状态监测与故障诊断技术及应用.北京:化学工业出版社,2003.
    3 何正嘉,等.机械设备非平稳信号的故障诊断原理及应用.北京:高等教育出版社,2001.
    4 何正嘉,等.现代信号处理及工程应用.西安:西安交通大学出版社,2007.
    5 余红英.齿轮振动信号分解及其在故障诊断中的应用.振动、测试与诊断,2005,(2):109-113.
    6 樊永生,郑钢铁.振动信号检测技术研究及其在故障诊断中的应用.应用力学学报,2006,23(3):388-392.
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李雅梅,陈明霞,杜晶.小波理论在滚动轴承故障诊断中的应用.计算机系统应用,2012,21(7):172-176

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History
  • Received:October 17,2011
  • Revised:November 12,2011
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