A new spectral average-based bearing fault diagnostic approach

Brandon Van Hecke, Yongzhi Qu, David He, Eric Bechhoefer

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations

Abstract

The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.

Original languageEnglish (US)
Pages (from-to)354-362
Number of pages9
JournalJournal of Failure Analysis and Prevention
Volume14
Issue number3
DOIs
StatePublished - Jun 2014
Externally publishedYes

Keywords

  • Bearing failure
  • Defect analysis
  • Mechanical component
  • Non-destructive testing
  • Rolling-element bearing

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