计算机应用研究2024，Vol.41Issue(10)：3044-3052,9.DOI:10.19734/j.issn.1001-3695.2024.02.0036
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
Fault diagnosis method via contrastive learning and neighborhood sample analysis under label noise
摘要
Abstract
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 net-work under the condition of label noise.In order to achieve accurate recognition of equipment operating conditions in the net-work 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.Iastly,the proposed method made use of label reweighting and con-sistency 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 cor-rect the noisy labels impressively well and maintain great fault diagnosis performance under the condition of high-level noisy labels.关键词
对比学习/标签噪声/标签纠正/故障诊断Key words
contrastive learning/noisy label/label correction/fault diagnosis分类
机械工程引用本文复制引用
金泽中,叶春明..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[J].计算机应用研究,2024,41(10):3044-3052,9.基金项目
上海市哲学社会科学一般项目(2022BGL010) （2022BGL010）
国家自然科学基金资助项目(71840003) （71840003）
上海市科学技术委员会"科技创新行动计划"软科学重点项目(20692104300) （20692104300）
