Abstract
To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.
Highlights
Of all existing sources of renewable energy, wind energy is considered to have by the European Union (EU) the largest potential to replace the fossil energy share in the 30 years [1]
The value per indicator is calculated as the average of the indicators from the number of consecutive time series from the vibration signals required to represent the time of one rotation of the shaft washer, which is dependent on the rotational speed
The classification accuracy scores for the trained models are summarized in Table 2, where two columns can be found for each indicator group: The first represents the averaged accuracy scores, and the second is the standard deviation of the accuracy scores of the 1000 combinations used, which can be used to enhance the description of the obtained results
Summary
Of all existing sources of renewable energy, wind energy is considered to have by the European Union (EU) the largest potential to replace the fossil energy share in the 30 years [1]. With an estimated required energy production ranging between 230 and 450 GW in Europe by 2050 [2], and with 60% of the investment in renewable energy projects in Europe focusing only on offshore wind projects [3], with the achievement of lower energy prices is a strategy to promote these projects. One strategy is condition-based maintenance (CBM), which can alleviate the O&M fixed cost when using a preventive maintenance approach. Conditionbased maintenance (CBM) is maintenance that is performed as governed by condition monitoring (CM) programs, which are the acquisition and processing of information and data that indicate the state of a part or machine over time. The diagnosis is fundamental to the achievement of CM programs
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