基于小波变换和优化CNN的风电齿轮箱故障诊断

Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN

针对传统故障诊断方法过于依赖人为经验的缺陷,提出小波变换和二维密集连接扩张卷积神经网络(WT-ICNN)的风电齿轮箱智能故障诊断方法.所提方法将一维振动信号通过连续小波变换(WT)转换成二维故障图像;再将二维故障图像输入ICNN中进行训练和测试.通过齿轮箱开源数据和风场实测数据验证结果表明,与传统故障诊断方法相比,所提方法采用密集连接的结构自适应特征提取时频图,有效加强了故障特征的利用效率;在对风电齿轮箱的故障诊断中,所提方法具有更好的特征复用能力和更高的诊断精度.

An intelligent fault diagnosis method for wind turbine gearbox based on wavelet transform and twodimensional densely connected dilated convolutional neural network(WT-ICNN)was proposed,aiming at the problem that traditional fault diagnosis method dependent on human experience too much.One dimensional vibration signal was transformed into two-dimensional fault image by continuous wavelet transform.Then the twodimensional fault image was inputted into ICNN for training and testing.The verification of open source data of gearbox and measured data of wind field showed that compared with the traditional fault diagnosis methods,the proposed method effectively enhanced the utilization efficiency of fault features by using the densely connected structure for adaptive feature extraction of time-frequency map.And in the fault diagnosis of wind power gearbox,the proposed method had better feature reuse ability and higher diagnosis accuracy.