Diagnosis of tidal turbine vibration data through deep neural networks

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Galloway, Grant S. and Catterson, Victoria M. and Fay, Thomas and Robb, Andrew and Love, Craig; Eballard, Ioana and Bregon, Anibal, eds. (2016) Diagnosis of tidal turbine vibration data through deep neural networks. In: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016. PHM Society, ESP, pp. 172-180. ISBN 9781936263219

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Abstract

Tidal power is an emerging field of renewable energy, harnessing the power of regular and predictable tidal currents. However, maintenance of submerged equipment is a major challenge. Routine visual inspections of equipment must be performed onshore, requiring the costly removal of turbines from the sea bed and resulting in long periods of downtime. The development of condition monitoring techniques providing automated fault detection can therefore be extremely beneficial to this industry, reducing the dependency on inspections and allowing maintenance to be planned efficiently. This paper investigates the use of deep learning approaches for fault detection within a tidal turbine's generator from vibration data. Training and testing data were recorded over two deployment periods of operation from an accelerometer sensor placed within the nacelle of the turbine, representing ideal and faulty responses over a range of operating conditions. The paper evaluates a deep learning approach through a stacked autoencoder network in comparison to feature-based classification methods, where features have been extracted over varying rotation speeds through the Vold-Kalma filter.

ORCID iDs

Galloway, Grant S. ORCID logoORCID: https://orcid.org/0000-0003-2861-6504, Catterson, Victoria M. ORCID logoORCID: https://orcid.org/0000-0003-3455-803X, Fay, Thomas, Robb, Andrew and Love, Craig; Eballard, Ioana and Bregon, Anibal
CORE (COnnecting REpositories)