关键词: 快速傅里叶变换, 深度神经网络, 齿轮箱, 预测

Abstract: Aiming at the non-linearity and correlation of the oil temperature value of the gearbox of wind turbines, in order to achieve accurate oil temperature prediction, a prediction method based on fast Fourier transform (FFT) and deep neural network (DNN) is proposed. First, the time series characteristics of the oil temperature data are analyzed, and the time window is selected to arrange the information. Then, FFT is performed on the information and its high-frequency amplitude characteristics are extracted, and these characteristics are input into the DNN model for training. Finally, an evaluation is made for the output results. The method is validated with measured data, and common models are selected for comparison. The results verify the effectiveness of the method. The method can provide early warning before the gearbox operating state is abnormal, reduce equipment functional failures, and reduce the loss of wind turbines due to failure and shutdown, and has practical value.

Key words: fast Fourier transform, deep neural network, gearbox, prediction