Feature extraction enhancement based on parameterless empirical wavelet transform: Application to bearing fault diagnosis

Heba El-Mongy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Rolling-element bearings are usually subject to faults that need prompt
detection in order to prevent sudden failures. Many time-frequency analysis
techniques have been used for the purpose of bearing fault detection and
diagnosis. From these techniques, wavelets and empirical mode
decomposition (EMD) stand out as the most widely applied methods in
bearing fault diagnosis. Recently, a novel method named the parameterless
empirical wavelet transform (PEWT) has been proposed to combine the
wavelet formulation with the adaptability of the empirical mode
decomposition. In this paper, the parameterless empirical wavelet transform
(PEWT) is combined with envelope detection (ED) to present a new scheme
named PEWT-ED for non-stationary signal analysis. The capabilities and
limitations of the new method in bearing fault diagnosis are investigated
using simulation and experiment. The results show that the new approach
can effectively extract the bearing fault characteristics. The PEWT-ED is
found to be a powerful tool in signal de-noising and enhancement for fault
diagnosis purposes.
Original languageEnglish
Title of host publicationProceedings of 18th International Conference on Applied Mechanics and Mechanical Engineering AMME 2018
PublisherAMME
Pages1-19
Number of pages19
Volume18
DOIs
Publication statusPublished - 30 Apr 2018
Event18th International Conference on Applied Mechanics and Mechanical Engineering -
Duration: 3 Apr 20185 Apr 2018

Conference

Conference18th International Conference on Applied Mechanics and Mechanical Engineering
Period3/04/185/04/18

Keywords

  • rotating machinery
  • fault diagnosis
  • empirical wavelet transform
  • signal processing

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