基于GADF和PAM-Resnet的旋转机械小样本故障诊断方法

在旋转机械的实际工作中,由于故障样本有限,很难实现准确的故障诊断.对此,提出一种基于GADF和PAM-Resnet的小样本故障诊断方法.首先,构建一种数据增强策略,该策略将数目较少的一维信号样本转化为二维GADF 图,之后将GADF图裁剪成多个子图,从而得到大量的图像样本,解决样本数目不足的问题;然后,构建一种位置注意力模块(PAM),该模块使用横向卷积和纵向卷积分别对横向特征和纵向特征赋予权重,融合两种特征得到GADF图的位置信息;最后,将PAM插入残差块中构建PAM残差块,并使用多个PAM残差块构建PAM- Resnet,PAM-Resnet可以有效地关注位置信息,具有较强的故障特征学习能力.分别进行小样本环境下的齿轮箱故障诊断和滚动轴承故障诊断实验,结果表明所提出方法具有较高的故障诊断准确率,可以准确地诊断出小样本环境下的故障类型.

Small sample fault diagnosis method for rotating machinery based on GADF and PAM-Resnet

In the actual work of rotating machinery, it is difficult to achieve accurate fault diagnosis because of the limited fault samples. To address this problem, a small-sample fault diagnosis method based on GADF and PAM-Resnet is proposed. Firstly, the proposed method constructs a data enhancement strategy, which converts a small number of 1D signal samples into 2D GADF images, and then crops the GADF images into multiple sub-images to obtain a large number of image samples, which solves the problem of insufficient number of samples. Then, a position attention model(PAM) is constructed, which uses horizontal and vertical convolution to give weights to horizontal features and vertical features, respectively, and fuses the two features to obtain the position information of the GADF image. Finally, the PAM is inserted into the residual block to construct the PAM residual block, and multiple PAM residual blocks are used to construct the PAM-Resnet. The PAM-Resnet can effectively focus on location information and has a strong fault feature learning capability. The fault diagnosis experiments of gearbox and rolling bearing under the small sample environment are carried out respectively, and the results indicate that the proposed method has higher fault diagnosis accuracy and can accurately diagnose the fault types under small sample environment.