Research on prediction model of antioxidant performance of wind turbine gearbox lubricating oil
This work aims to establish a method for rapidly determining the antioxidant properties of lubri-cating oil(gear oil)in wind turbine gearboxes using infrared spectroscopy.Based on the infrared spectral data of wind turbine gear oil,a series of data processing steps including sample set partitioning,data pre-processing,characteristic wavelength extraction and machine learning were sequentially performed.Final-ly,a variety of evaluation indexes were used to comprehensively evaluate the performance of the combined model.The results indicate that the partial least squares regression model established using spectral data preprocessed with standard normal variate(SNV)transformation performs the best.Among the two feature wavelength extraction methods,principal component analysis(PCA)demonstrates superior dimensionality reduction compared to the successive projections algorithm(SPA).Among the three kinds of machine learning,BP neural network has the best prediction effect.The final result indicates that the SNV+PCA+BP model has the best prediction effect,which can better and quickly predict the oxidation resistance of wind turbine gear oil.
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