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Autoregressive model-based diagnostics for gears and bearings

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The autoregressive (AR) model-based fault diagnosis approach employs an AR model of the gear signal as a linear prediction filter. For gear diagnosis, the synchronous signal average is processed by the AR filter with the localised gear fault information being contained in the prediction error signal (residual signal). For bearing diagnosis, the synchronously resampled (not averaged) signal in the angle domain is processed by the AR filter. The non-synchronous bearing fault signal should remain in the residual signal. This paper presents the theoretical background of the AR modelling method and the approaches to gear and bearing diagnosis using the AR modelling method. Application examples of detecting localised gear and bearing faults, such as a gear tooth crack and a bearing raceway spall, are also presented. This approach can be readily applied to fault diagnosis for complex mechanical systems such as helicopter main transmission gearboxes and turbine engines.
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Document Type: Research Article

Affiliations: Air Vehicles Division, Defence Science and Technology Organisation (DSTO), Fishermans Bend, Melbourne, VIC 3207, Australia.

Publication date: August 1, 2008

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