Galati, F. Antonio, Forrester, B. David and Dey, Subhrakanti (2006) Application of the Generalised likelihood ratio algorithm to the detection of a bearing fault in a helicopter transmission. Engineering Asset Management. pp. 400-405.
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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.
Item Type: | Article |
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Keywords: | Generalized Likelihood Ratio; GLR; ARMA; Fault Detection; Bearing Fault; Epicyclic Gear Train; Vibration Analysis; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering |
Item ID: | 12719 |
Identification Number: | 10.1007/978-1-84628-814-2_44 |
Depositing User: | Subhrakanti Dey |
Date Deposited: | 09 Apr 2020 09:56 |
Journal or Publication Title: | Engineering Asset Management |
Publisher: | Springer |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/12719 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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