标签噪声下结合对比学习与邻域样本 分析的故障诊断方法.

Nowadays due to the dependence of fault diagnosis method based on deep learning on well-labeled training dataset, which will lead to the problem that deep neural network can easily overfit those noisy labels and affect the generalization of network under the condition of label noise. In order to achieve accurate recognition of equipment operating conditions in the network trained with label noise, this paper proposed a fault diagnosis method via contrastive learning and neighborhood sample analysis. Firstly, the method used contrastive learning to pre-train the model, which could reduce the embedding distance of similar samples in the feature space and achieved improving the ability of optimizing the feature representation ability of the network. Then, the method utilized the feature similarity to find each sample's closest neighbors to estimate the reliability of training labels which could separate all training samples into a clean or noisy subset and implemented label correction on noisy subset. After that, it established a more reliable training subset. Lastly, the proposed method made use of label reweighting and consistency regularization to enhance robustness of network. In particular, two networks got trained simultaneously where each network used the dataset division from the other network during the training process, which could mitigate confirmation bias caused by single network model training framework. The experimental results on public dataset demonstrate that proposed method can verify and correct the noisy labels impressively well and maintain great fault diagnosis performance under the condition of high-level noisy labels. [ABSTRACT FROM AUTHOR]