一种基于对称极坐标优化的轴承深度迁移诊断方法

A BEARING DEEP LEARNING TRANSFER DIAGNOSIS METHOD BASED ON OPTIMIZATION OF SYMMETRIC POLAR COORDINATES

针对一维机械振动信号的图形化特征表示问题,引入了对称极坐标表示法,同时结合卷积神经网络强大的图像分类识别能力,提出了一种基于对称极坐标和残差网络迁移学习的轴承故障诊断方法。为突显轴承振动信号故障特征并兼顾计算效率,利用对称极坐标表示法将一维机械振动信号快速转换成镜面对称雪花图,利用NSGA-II同步优化了数据采样长度和对称极坐标表示法的参数,获取可区分性更好的极坐标对称图像特征,然后利用残差网络进行迁移学习的训练和分类,结合美国西储大学轴承公开数据集对此方法进行验证,取得了良好的识别效果。

Aiming at the problem of graphical feature representation of one-dimensional mechanical vibration signals, a bearing fault diagnosis method based on symmetric polar coordinates and residual network migration learning is proposed, which combines the powerful image classification and recognition ability of convolution neural network. Therefore, a bearing fault diagnosis method based on symmetric polar coordinates and residual network transfer learning is proposed in this paper. In order to highlight the fault characteristics of bearing vibration signals and take into account the calculation efficiency, the proposed method uses the symmetric polar coordinate method to convert the one-dimensional mechanical vibration signal into a mirror-symmetric snowflake map quickly and the transformation parameters and data sampling length are optimized synchronously by NSGA-II to obtain the image features with better distinguishability. Then, the transfer learning of residual network is used to train and classify. The bearing dataset of Case Western Reserve University which includes different rotational speeds and load is used to verify this method and a good recognition effect has been achieved.