Cross-Domain Fault Diagnosis of Bearing Based on Data Augmentation and Domain Generalization
In the practical fault diagnosis tasks,the target task is often unknown in advance,existing transfer learning methods mostly focus on learning from a single data source when constructing transfer models,they heavily rely on the quantity of samples in the target domain.In view of this problem,a fault diagnosis method was proposed based on data augmentation and domain generalization.A data preprocessing method was introduced to transform 1D vibration signals into a 2D feature indicator grayscale image.A deep condi-tional Wasserstein generative adversarial network with gradient penalty was proposed to augment the data from multiple source domains.Finally,a multi-source domain adversarial learning strategy was adopted to reduce the distribution differences among the multiple source domains,achieving feature domain adaptation for each source domain.The effectiveness and reliability of the proposed method were thor-oughly validated on a bearing dataset.Experimental results demonstrate the proposed method has high stability and generalization per-formance,and better diagnostic accuracy than other methods.
data augmentationdomain generalizationgenerative adversarialconvolutional neural networkcross-domain fault diagnosis
万方数据
维普
