Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique

Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, David K. Harrison, Steven Grainger

Research output: Contribution to journalArticle

Abstract

Machinery failure diagnosis is an important
component of the condition based maintenance (CBM)
activities for most engineering systems. Rolling element
bearings are the most common cause of rotating machinery
failure. The existence of the amplitude modulation and
noises in the faulty bearing vibration signal present
challenges to effective fault detection method. The wavelet
transform has been widely used in signal de-noising, due to
its extraordinary time-frequency representation capability.
In this paper, a new technique for rolling element bearing
fault diagnosis based on the autocorrelation of wavelet denoised
vibration signal is applied.

Original languageEnglish
Pages (from-to)393-402
Number of pages10
JournalInternational Journal of Advanced Manufacturing Technology
Volume40
Issue number3-4
DOIs
Publication statusPublished - 1 Jan 2009

Keywords

  • autocorrelation
  • impulse-response wavelet
  • wavelet de-noising
  • bearing fault detection
  • Kurtosis maximization

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