On the use of AI based vibration condition monitoring of wind turbine gearboxes

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Koukoura, Sofia and Carroll, James and McDonald, Alasdair (2019) On the use of AI based vibration condition monitoring of wind turbine gearboxes. Journal of Physics: Conference Series, 1222 (1). 012045. ISSN 1742-6588 (https://doi.org/10.1088/1742-6596/1222/1/012045)

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Abstract

Condition monitoring (CM) systems are installed in wind turbines (WTs) in order to avoid component downtime and reduce maintenance costs. Vibration monitoring is widely used for the WT gearbox, which is a component with a significant downtime. Given that the installed wind capacity grows, the volume of CM data increases, making manual interpretation of vibration signals challenging. Therefore, there is a need for an efficient and automated maintenance decision support system. The aim to this paper is to propose an automated framework for gearbox incipient failure diagnosis. The framework utilises vibration signals and performs health estimation and fault isolation based on signal processing and artificial intelligence (AI) techniques. The methodology is demonstrated through a case study of vibration data from operating WTs with known gearbox failures. The study can be used to optimise wind turbine maintenance actions.

ORCID iDs

Koukoura, Sofia, Carroll, James ORCID logoORCID: https://orcid.org/0000-0002-1510-1416 and McDonald, Alasdair ORCID logoORCID: https://orcid.org/0000-0002-2238-3589;
CORE (COnnecting REpositories)