Drive-train condition monitoring for offshore wind and tidal turbines

Sanaz Roshanmanesh, Farzad Hayati, Vassilios Kappatos, Fausto Pedro Garcia Marquez, Alberto Pliego Marugán, Carlos Quiterio Gómez Muñoz, Cem Selcuk, Tat-Hean Gan, Mayorkinos Papaelias

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Offshore wind and tidal turbines are complex systems consisting of several different components and subsystems. One of the most important components is the drive-train. Gearboxes in geared designs are designed to operate for the entire lifetime of a wind or tidal turbine or the equivalent of 25 years. However, very few gearboxes seem to be able to achieve their intended lifetime without significant refurbishment or even replacement within this period. Gearboxes in offshore wind turbines have been experiencing relatively high failure rates. The impact of unexpected gearbox failure is severe as it results in considerable downtime and hence loss of energy production. Moreover, unplanned maintenance and gearbox replacement and rebuild add to the overall costs that need to be incurred. Accessibility issues particularly in offshore wind and tidal farms add to the challenges that need to be overcome. The benefit of precise and reliable CM is the early detection and evaluation of faults enabling not only the avoidance of unexpected catastrophic failure of the gearbox but also efficient maintenance scheduling. Thus a noteworthy reduction of Operation and Maintenance (O&M) costs can be realised. Gearboxes are subject to several damage mechanisms which may lead to various failure modes including gear teeth damage, cracking of the gearbox case, shaft misalignment, wear or looseness of torque arm, loss of lubricant in lubrication system, bearing damage and shaft failure. This paper presents an experimental investigation assessing the effectiveness of Acoustic Emission (AE) and vibration analysis (VA) in identifying different types of faults in wind and tidal turbine drive-trains. Additionally the application of advanced signal processing techniques, such as Spectral Kurtosis (SK) and wavelet analysis have been studied on AE and VA signals to assess their effectiveness.
Publikationsdato24. okt. 2016
StatusUdgivet - 24. okt. 2016
Begivenhed2nd International Conference on Renewable Energies Offshore (Renew 2016) - Lisbon, Portugal
Varighed: 24. okt. 201626. okt. 2016


Konference2nd International Conference on Renewable Energies Offshore (Renew 2016)


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