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A promising architecture to detect bearing failures in wind-turbine gearboxes has been presented that comprises a combination of angular resampling for vibration analysis. monitoring of the wind-turbine power output and data-mining techniques for the classification task. To overcome the lack of datasets with a wide range of conditions of loads and speeds. different experiments have been performed on a test-bed for two common failure mechanisms: misalignment (two levels of failure) and imbalance (five levels of failure). in addition to the non-fault case. The whole dataset included 6551 instances with 544 variables. such that it can be considered a high dimensional problem. The dataset was mainly balanced within the seven fault cases and the non-fault case. the contribution of each one to the final data set being higher than 8.4% and lower than 13.5%.
