China Mechanical Engineering ›› 2011, Vol. 22 ›› Issue (17): 2080-2084.

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Bearing Multi-fault Diagnosis Based on Improved Template Denoising Source Separation

Chen Xiaoli1;Wang Zhongsheng1;Jiang Hongkai1;Wang Feng2
  

  1. 1.Northwestern Polytechnical University, Xi’an, 710072
    2.South-western Explortion & Development Company,Tarim Oilfield,Kashi,Xinjiang,844804
  • Online:2011-09-10 Published:2011-09-14
  • Supported by:
    National Natural Science Foundation of China(No. 51075330,50975231)

基于改进样板去噪源分离的轴承复合故障诊断

陈晓理1;王仲生1;姜洪开1;王峰2
  

  1. 1.西北工业大学,西安,710072
    2.塔里木油田塔西南勘探开发公司,喀什,844804
  • 基金资助:
    国家自然科学基金资助项目(51075330,50975231)
    National Natural Science Foundation of China(No. 51075330,50975231)

Abstract:

Processing noise improperly or the number of sensors less than the numner of faults may lead to failure of multi-fault diagnosis based on source separation.Improved template denoising source separation was proposed and used to diagnose multi-fault of bearings.Shift independent component analysis was carried out first and the independent components obtained were used as templates to the template denoising source separation algorithm.The proposed method can separate source signals through underdetermined source under reverberant environments and diagnose multi-fault of bearings.The effectiveness was verified by both simulated and experimental anlysis.

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摘要:

环境噪声处理不当或者传感器数目少于故障数目都可能导致基于源分离的复合故障诊断失败,针对此问题,提出了基于改进样板去噪源分离的诊断方法并用于轴承复合故障诊断。该方法首先对数据进行移动独立分量分析,然后将得到的独立成分作为输入样板进行样板去噪源分离。轴承复合故障的仿真和实验数据分析证明,该方法能够在未知噪声环境下完成传感器数目少于故障数目的欠定源分离,该方法对轴承复合故障诊断是有效的。

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