基于一维卷积神经网络和Soft-Max分类器的风电机组行星齿轮箱故障检测

Fault Detection of Wind Turbine Planetary Gear Box Using 1D Convolution Neural Networks and Soft-Max Classifier

将卷积神经网络引入风机故障检测领域,设计了一种一维卷积神经网络的结构,并和Soft-Max分类器相结合构造了一种双层智能诊断架构。一维卷积神经网络用于行星齿轮箱数据的特征提取,Soft-Max分类器对提取的特征进行分类。与传统智能算法相比,该方法具有训练样本少,可直接使用原始数据训练网络;计算效率高,可以适应实时诊断的需要。试验结果证明,该方法可以有效地诊断出不同工况下的行星齿轮箱中的齿轮故障。

The convolutional neural network was introduced into the field of fan fault detection for the first time,a new method based on one dimensional convolution neural network（ CNNs） and Soft-Max classifier was proposed,which was applied to the fault diagnosis of gearbox planetary gear under different operating conditions. The structure of the network was a double layer structure,the improved convolutional neural network was used for feature extraction,and the Soft-Max classifier was used to classify the health status of the signal. Compared with the traditional intelligent algorithm,this method had the advantages of fewer training samples,direct training of network with raw data,high computational efficiency,and it can meet the needs of real-time diagnosis. The data of multi operating conditions are fused and verified by experiments. The experimental results showed that the method can effectively diagnose the gear faults in planetary gear box under different working conditions.