Gear fault detection using acoustic emission spectrum kurtosis

Yongzhi Qu, Junda Zhu, David He, Eric Bechhoefer

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Even though acoustic emission (AE) has been studied as a potential information source for machine fault diagnosis over many years, applications of AE sensors to gear fault diagnosis have been limited due to the lack of effective and efficient AE data analysis techniques. In this paper, a frequency reduction technique is proposed to preprocess the AE signals. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. After preprocessing, time synchronous average (TSA) is calculated and spectrum kurtosis (SK) is estimated for the calculated TSA signals as AE features for gear fault detection. An optimal band pass filter based on SK is designed to filter the signals and to extract features for fault detection. The presented method is validated using seeded gear fault tests on a notational split torque gearbox. The method presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

Original languageEnglish (US)
StatePublished - Sep 25 2013
Externally publishedYes
EventJoint Conference on 67th Machinery Failure Prevention Technology, MFPT 2013 and 59th International Society of Automation, ISA 2013 - Cleveland, OH, United States
Duration: May 13 2013May 17 2013

Conference

ConferenceJoint Conference on 67th Machinery Failure Prevention Technology, MFPT 2013 and 59th International Society of Automation, ISA 2013
CountryUnited States
CityCleveland, OH
Period5/13/135/17/13

Keywords

  • Acoustic emission
  • Diagnosis
  • Gear fault
  • Kurtogram
  • Spectrum kurtosis
  • T-statistics
  • Time synchronous average

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