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Prognosis and fault detection of drivetrains in medium-speed 10-MW Floating Wind Turbines

Yagüe, Adrián
Master thesis
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no.ntnu:inspera:54167677:47039432.pdf (13.73Mb)
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https://hdl.handle.net/11250/2780173
Utgivelsesdato
2020
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  • Institutt for marin teknikk [2885]
Sammendrag
 
 
Premature failures in large offshore Wind Turbines are often attributed to bearing failure despite gearboxes

being designed and developed using the best bearing design practices. Furthermore, as turbine size and

rated power increase, bearings display an enhanced tendency to fail. Unscheduled bearing replacement at

sea is a complex, costly, weather-dependent and time-consuming operation that results in high turbine

downtimes. Market trends show an increase in turbine rated capacity and a noticeable shift towards deeper

waters and far-off remote sites which further delays and complicates unscheduled maintenance activities

and aggravates the cost penalties of idle turbines. Detecting an incipient bearing fault (diagnosis task) is

therefore a major aspect to evaluate drivetrain and overall wind turbine reliability. Moreover, estimating the

remaining useful life of bearings and predicting their operational state in the future (prognosis task) can

achieve a breakthrough in optimizing maintenance programs, improve wind farm operation and decrease

wind turbine downtime which can bring about a significant cost reduction.

The purpose of this work is to investigate the health monitoring and prognostics possibilities of drivetrain

bearings in a floating spar-buoy offshore wind turbine. The drivetrain concept considered in this work is

based on DTU's 10-MW reference wind turbine. Specifically, this study targets the prognosis of four critical

drivetrain bearings located in the main shaft and the high-speed shaft. The absence of run-to-failure data

of real wind farms, although inconvenient, is overcome by using model-generated degradation data. A high-fidelity numerical twin of a state-of-the-art drivetrain concept is used in this work and is established using a

multi-body system (MBS) approach. The numerical twin models a medium-speed 10-MW gearbox that

consists of 3 stages, 2 planetary stages and 1 parallel stage, supported in a 4-point configuration layout

with two main bearings and two torque arms. The drivetrain concept studied in this work uses a novel

selection of bearings which is currently gaining traction in large offshore wind turbines. The two main

bearings that support the main shaft are tapered roller bearings (TRB) that carry both axial and radial loads

as opposed to the main bearings used in traditional high-speed gearbox designs which typically use a

cylindrical roller bearing to carry radial loads and a spherical roller bearing to carry axial loads.

Faults are applied on the main bearing and on the high-speed shaft bearings of the numerical model. The

model-generated degradation data, namely forces and acceleration measurements at several shafts and

bearings, is used as input data for two independent prognosis models: a physics-based prognosis model

and a data-driven prognosis model. The physics-based approach will culminate in a prediction of the

remaining useful life (RUL) of several bearings under a range of faults. The fault detection and fault

prognosis capabilities of the proposed prognosis methods is evaluated and compared. This work will also

assess the merits and limitations of using model-generated degradation data for the development of

prognosis models. Lastly, based on this study, the requirements to enable bearing prognosis from a purely

data-driven approach, as opposed to a physics-based approach, is put forth.
 
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