Journal of Central South University

, Volume 24, Issue 2, pp 478–488 | Cite as

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

  • Chun-sheng Wang (王春生)
  • Chun-yang Sha (沙春阳)
  • Mei Su (粟梅)
  • Yu-kun Hu (胡玉坤)
Article
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Abstract

An improved ensemble empirical mode decomposition (EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

Key words

locomotive bearing vibration signal enhancement self-adaptive EEMD parameter-varying noise signal feature extraction 
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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Chun-sheng Wang (王春生)
    • 1
  • Chun-yang Sha (沙春阳)
    • 1
  • Mei Su (粟梅)
    • 1
  • Yu-kun Hu (胡玉坤)
    • 2
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.School of EngineeringUniversity of WarwickCoventryUK

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