基于FFT与DNN的齿轮箱油温数据预测

针对风电机组齿轮箱油温数值的非线性与相关性，为实现油温的准确预测，提出一种基于快速傅里叶变换(Fast Fourier Transform, FFT)的深度神经网络(Deep Neural Network, DNN)的预测方法。首先，对油温数据进行时间序列特性分析，选择时间窗口对信息进行排列，然后对信息进行FFT并提取其高频幅特征，并把这些特征输入DNN模型中进行训练，最后对输出的结果进行评价。采用实测数据对该方法进行验证，并选用常见模型进行对比，结果验证了该方法的有效性。该方法可以在齿轮箱运行状态异常前预警，降低设备功能性的故障，减少风电机组故障停机的损失，具有实用价值。

Prediction of Gearbox Oil Temperature Based on FFT and DNN

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.