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This paper proposes an oil/lubrication level recognition (LLR) approach for a gearbox of a wind power system. The approach can recognize the lubrication level of a gearbox by only using generator current without any additional measurement apparatus such as accelerometers for measuring
vibration signals. First, in the study, the 11 lubrication levels from a full to completely empty were tailor-made at every 10% intervals. The generator current signals of the lubrication levels of the gearbox were measured by using the dynamometer test bed. Second, the frequency spectrums
of the current signals were generated via Fast Fourier Transform (FFT) and several features were extracted from the FFT spectrums. Finally, the recognition accuracies obtained by using the k-nearest neighbor (KNN) and back propagation (BP) are discussed based on the optimal feature
combination. The results indicate that the KNN-based LLR approach can efficiently recognize lubrication levels even under noise interference.
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