Wind turbine gearbox planet bearing failure prediction using vibration data
Koukoura, S and Carroll, J and McDonald, A and Weiss, S (2018) Wind turbine gearbox planet bearing failure prediction using vibration data. Journal of Physics: Conference Series, 1104. 012016. ISSN 1742-6596 (https://doi.org/10.1088/1742-6596/1104/1/012016)
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Abstract
This paper presents a methodology for predicting planet bearing failures utilising vibration data acquired through accelerometers installed on the gearbox surface. The proposed methodology applies certain signal pre-processing techniques in order to remove the speed variations of the turbine and separate the stochastic bearing components from the deterministic gear ones. Then, spectral kurtosis is used to enhance the impulsiveness of the bearing fault signatures and envelope analysis is used to demodulate the signal. Features are extracted from the envelope spectrum and are used as an input to a classification model. The classification labelling is performed based on the time before failure. The methodology is tested on real offshore wind turbine vibration data collected at various times before failure. The performance of the classifier is assessed using k-fold cross validation. The results are compared with methods of classic envelope analysis that uses a constant demodulation band.
ORCID iDs
Koukoura, S, Carroll, J ORCID: https://orcid.org/0000-0002-1510-1416, McDonald, A ORCID: https://orcid.org/0000-0002-2238-3589 and Weiss, S ORCID: https://orcid.org/0000-0002-3486-7206;-
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Item type: Article ID code: 66053 Dates: DateEvent6 November 2018Published6 November 2018Published Online4 October 2018AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 09 Nov 2018 11:07 Last modified: 02 Oct 2024 00:31 URI: https://strathprints.strath.ac.uk/id/eprint/66053