ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK - ZHANG LiZhi; XU WeiXiao; JING LuYang; TAN JiWen (Jixie qiangdu)
In order to extract effective features of complex signals,a fault diagnosis method combining short-time Fourier transform and two-dimensional convolution neural network is proposed. First,a short-time Fourier transform is performed on the rotating mechanical vibration signal to obtain a time-frequency map. Then,the time-frequency map is input into a two-dimensional convolution neural network for identification,and a final classification result is obtained. The method is applied to the fault diagnosis of rolling bearing and gearbox,and has achieved good results in the data of the Case Western Reserve University and the PHM2009 dataset.The correct rate is better than the direct comparison of the original signal into CNN,which verifies the superiority of the method.