Earticle

초록

영어

The rolling bearing vibration is a complex, non-stationary and dynamic process. The vibration signal can not be described through a fixed time or frequency function. This increases the difficulty of fault diagnosis. Therefore, this paper proposed a rolling bearing fault diagnosis method based on local mean decomposition (LMD) and time-frequency analysis. Firstly, we need to draw time-domain waveform and power spectrum of vibration signal in this method. Secondly, we can gain the energy density distribution of signal using directly time-frequency analysis of signal, the time-frequency analysis of intrinsic mode function (IMF) and the time-frequency analysis of production function (PF). We gain the IMF by decomposing the original signal by the ensemble empirical mode decomposition (EEMD).and after the original signal is decomposed by the local mean decomposition,we gain the PF .Finally, according to the energy density distribution of signal, diagnose the fault of the rolling bearing. The simulation results show that we can gain the clearer the energy density distribution using the time-frequency of PF of signal to diagnose fault and improve the reliability of the fault diagnosis

목차

Abstract
 1. Introduction
 2. The Decomposition Principle of EEMD and LMD
  2.1. The decomposition principle of EEMD
  2.2. The decomposition principle of LMD
 3. Time-frequency Analysis
  3.1. Short-time Fourier Transform
  3.2. Cohen time-frequency distribution
 4. Experimental Simulation Demonstration
  4.1. The vibration signal analysis of the normal rolling bearing
  4.2. The vibration signal analysis of the inner ring fault
  4.3. The vibration signal analysis of the rolling element fault
  4.4. The vibration signal analysis of the outer ring fault
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Jun Ma [ Faculty of Information Engineering and Automation, Kunming University of Science and Technology ]
• Jiande Wu [ Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Engineering Research Center for Mineral Pipeline Transportation YN ]
• Xuyi Yuan [ Faculty of Information Engineering and Automation, Kunming University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605