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

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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.

    Creators(s): Koukoura, S, Carroll, J ORCID logoORCID: https://orcid.org/0000-0002-1510-1416, McDonald, A ORCID logoORCID: https://orcid.org/0000-0002-2238-3589 and Weiss, S ORCID logoORCID: https://orcid.org/0000-0002-3486-7206;
    Item type:Article
    ID code:66053
    Keywords:wind turbines, planetary gearboxes, wind power, reliability and maintenance modelling, vibration signals, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Technology and Innovation Centre > Sensors and Asset Management
    Depositing user: Pure Administrator
    Date deposited:09 Nov 2018 11:07
    Last modified:16 Jul 2020 03:18
    URI:https://strathprints.strath.ac.uk/id/eprint/66053
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