Eemd-based steady-state indexes and their applications to condition monitoring and fault diagnosis of railway axle bearings

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

28 Scopus Citations
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Author(s)

  • Cai Yi
  • Dong Wang
  • Wei Fan
  • Kwok-Leung Tsui
  • Jianhui Lin

Detail(s)

Original languageEnglish
Article number704
Journal / PublicationSensors (Switzerland)
Volume18
Issue number3
Online published27 Feb 2018
Publication statusPublished - Mar 2018

Link(s)

Abstract

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.

Research Area(s)

  • A shape function, Multiple bearing defects, Railway axle bearing fault diagnosis, Steady-state index, The coefficient of variation, Threshold

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