A new acoustic emission sensor based bearing fault diagnostic technique

Brandon Van Hecke, Yongzhi Qu, David He

Research output: Contribution to conferencePaperpeer-review

Abstract

Bearing fault diagnosis by quantifying acoustic emission (AE) data has been an area of interest for recent years due to the numerous advantages over vibration based techniques. However, most AE based methodologies to date are data-driven technologies. This research takes a new approach combining a heterodyne based frequency reduction technique, time synchronous resampling (TSR), and spectral averaging to process AE signals and extract condition indicators (CIs) for bearing fault diagnosis. The heterodyne technique allows the AE signal frequency to be shifted from several MHz to less than 50 kHz, which is comparable to that of vibration based techniques. Then, the digitized 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 AE data to allow the computation of a spectral average and the extraction of CIs for bearing fault diagnosis. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The result is an effective physics based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage.

Original languageEnglish (US)
StatePublished - 2014
Externally publishedYes
Event68th Society for Machinery Failure Prevention Technology Conference: Technology Solutions for Affordable Sustainment, MFPT 2014 - VA, United States
Duration: May 20 2014May 22 2014

Conference

Conference68th Society for Machinery Failure Prevention Technology Conference: Technology Solutions for Affordable Sustainment, MFPT 2014
Country/TerritoryUnited States
CityVA
Period5/20/145/22/14

Keywords

  • Acoustic Emission Sensor
  • Bearing
  • Fault Diagnosis

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