基于卷积神经网络的时频图像识别研究

摘要

As a key part of the vehicle power transmission system,the transmission can directly affect the performance of an automobile by its vibration and noise.In many cases,the rotational speed of the in-put shaft of gearbox is changing,which adds to the complexity of the fault diagnosis.In response to this problem,the study presents a new method for the gearbox fault identification and classification based on convolutional neural network.The vibration signals of the gearbox under various fault conditions are col-lected,and all kinds of signals are transformed to time-frequency images by using the time-frequency a-nalysis.Then the time-frequency matrices are input to the CNN for classification of different types of faults.And the recognition performance of CNN combined with different time-frequency analysis methods is studied.The results show that the method of CWT and CNN has the best performance in time-frequen-cy image recognition with variable rotational speed of gearbox.%变速器作为汽车动力传递系统中的关键部件,其振动和噪声直接影响着汽车的性能.由发动机输入到变速器的转速很多情况下是变化的,这使得这种工况下的变速器故障诊断更加复杂.针对这个问题,提出了基于卷积神经网络(convolutional neural network,CNN)的变速器变转速工况下的故障分类识别方法:在变转速下,采集了变速箱多种故障状态下的振动信号,对各类信号进行时频变换得到时频矩阵,并利用CNN实现多类故障的分类.并研究了CNN结合不同时频方法时的识别性能,结果表明,连续小波变换(continuous wavelet transform,CWT)与CNN结合的方法对变转速下的时频图识别性能最好.