Wind turbine gearbox vibration signal signature and fault development through time

Koukoura, Sofia and Carroll, James and Weiss, Stepha and McDonald, Alasdair; (2017) Wind turbine gearbox vibration signal signature and fault development through time. In: 25th European Signal Processing Conference, EUSIPCO 2017. IEEE, GRC, pp. 1380-1384. ISBN 9780992862671 (https://doi.org/10.23919/EUSIPCO.2017.8081435)

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Abstract

This paper aims to present a methodology for health monitoring wind turbine gearboxes using vibration data. Monitoring of wind turbines is a crucial aspect of maintenance optimisation that is required for wind farms to remain sustainable and profitable. The proposed methodology performs spectral line analysis and extracts health features from harmonic vibration spectra, at various time instants prior to a gear tooth failure. For this, the tachometer signal of the shaft is used to reconstruct the signal in the angular domain. The diagnosis approach is applied to detect gear faults affecting the intermediate stage of the gearbox. The health features extracted show the gradient deterioration of the gear at progressive time instants before the catastrophic failure. A classification model is trained for fault recognition and prognosis of time before failure. The effectiveness of the proposed fault diagnostic and prognostic approach has been tested with industrial data. The above will lay the groundwork of a robust framework for the early automatic detection of emerging gearbox faults. This will lead to minimisation of wind turbine downtime and increased revenue through operational enhancement.

ORCID iDs

Koukoura, Sofia, Carroll, James ORCID logoORCID: https://orcid.org/0000-0002-1510-1416, Weiss, Stepha ORCID logoORCID: https://orcid.org/0000-0002-3486-7206 and McDonald, Alasdair ORCID logoORCID: https://orcid.org/0000-0002-2238-3589;
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