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Application of the Generalised Likelihood Ratio Algorithm to the Detection of a Bearing Fault in a Helicopter Transmission

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Abstract

A Bell 206B main rotor gearbox was run at high load under test conditions in the Helicopter Transmission Test Facility operated by the Defence Science and Technology Organisation (DSTO) of Australia. The test succeeded in initiating and propagating pitting damage in one of the planet gear support bearings. Vibration acceleration signals were recorded periodically for the duration of the test. The time domain vibration signals were converted to angular domain to minimise the effects of speed variations. Auto-Regressive Moving-Average (ARMA) models were fitted to the vibration data and a change detection problem was formulated in terms of the Generalised Likelihood Ratio (GLR) algorithm. Two different forms of the GLR algorithm in window-limited online form were applied. Both methods succeeded in detecting a change in the vibration signals towards the end of the test. A companion paper submitted by the University of New South Wales outlines the corresponding diagnosis and prognosis algorithms applied to the vibration data.

Key Words

  • Generalized Likelihood Ratio
  • GLR
  • ARMA
  • Fault Detection
  • Bearing Fault
  • Epicyclic Gear Train
  • Vibration Analysis

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  • DOI: 10.1007/978-1-84628-814-2_44
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© 2006 CIEAM/MESA

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Antonio Galati, F., David Forrester, B., Dey, S. (2006). Application of the Generalised Likelihood Ratio Algorithm to the Detection of a Bearing Fault in a Helicopter Transmission. In: Mathew, J., Kennedy, J., Ma, L., Tan, A., Anderson, D. (eds) Engineering Asset Management. Springer, London. https://doi.org/10.1007/978-1-84628-814-2_44

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  • DOI: https://doi.org/10.1007/978-1-84628-814-2_44

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-583-7

  • Online ISBN: 978-1-84628-814-2

  • eBook Packages: EngineeringEngineering (R0)