基于卷积神经网络的多层域自适应滚动轴承故障诊断

针对基于卷积神经网络(CNN)的域自适应技术在提取可迁移特征的训练过程中,存在内部协变量移位的问题,提出一种多层域自适应滚动轴承故障诊断方法。首先,利用CNN提取原始振动数据的可迁移特征;其次,提出了多层域自适应和权重正则化项约束CNN参数,进一步减少可迁移特征的分布差异,从而解决域移位问题;最后,利用凯斯西储大学的滚动轴承数据集进行实验验证。结果表明,该方法能够有效地减少源域和目标域之间的特征分布差异,提高CNN模型对目标域数据集的诊断性能,相对于最高层域自适应的故障诊断方法,所提方法能在两个数据集之间的迁移故障诊断中得到较高的分类识别结果。

Multi domain adaptive rolling bearing fault diagnosis based on convolutional neural network

This paper aims at the problem of internal covariate displacement in the training process of extracting transferable features based on the convolutional neural network (CNN) domain adaptive technology. A multi domain adaptive rolling bearing fault diagnosis method is proposed. First, use CNN to extract the migratable features of the original vibration data; Secondly, multi layer domain adaptation and weight regularization terms are used to constrain CNN parameters to further reduce the distribution difference of migratable features, thereby solving the problem of domain shift; Finally, the rolling bearing data set of Case Western Reserve University was used for experimental verification. The results show that this method can effectively reduce the difference in feature distribution between the source domain and the target domain, and improve the diagnostic performance of the CNN model on the target domain dataset, compared with the adaptive fault diagnosis method at the highest level, the proposed method can achieve higher classification and recognition results in the fault diagnosis of migration between two data sets.