基于小波变换和优化CNN的风电齿轮箱故障诊断

An intelligent fault diagnosis method for wind turbine gearbox based on wavelet transform and two-dimensional 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 two-dimensional 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.