Abstract Limited condition monitoring data are recorded with label information in practice, which make the fault identification task more challenging. A semi-supervised learning (SSL) approach can be employed to increase the identification performance of the classifiers under such situation. In this study, a three-stage SSL approach using data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis under limited labeled data. In the first stage, a DA method comprising seven DA strategies is presented to expand the feature space for the limited labeled samples under each healthy conditions. An optimization objective combining a cross entropy loss and a triplet loss is adopted to enlarge the margin between the feature distributions of limited labeled samples under different healthy conditions. In the second stage, a K-means technique is employed to acquire the cluster centers for the limited labeled samples under different healthy conditions. In the third stage, the label information for the unlabeled samples is first estimated according to the membership between the feature distributions of the unlabeled samples and the various cluster centers for original labeled samples and then a Kullback-Leibler divergence loss is introduced to minimize the discrepancy between feature distributions for the unlabeled samples and its corresponding cluster centers. The effectiveness of the proposed method is evaluated on two case studies, one is on an experimental bearing fault dataset from our laboratory test-rig, and the other is on a publicly dataset from a bearing degradation test. The comparison results on these two case studies demonstrate that the proposed method can perform better in bearing fault diagnosis under limited labeled samples than existing diagnostic methods.

中文翻译：

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基于数据增强和度量学习的滚动轴承智能故障诊断多阶段半监督学习方法

摘要 在实践中，有限的状态监测数据用标签信息记录，这使得故障识别任务更具挑战性。在这种情况下，可以采用半监督学习 (SSL) 方法来提高分类器的识别性能。在这项研究中，提出了一种使用数据增强 (DA) 和度量学习的三阶段 SSL 方法，用于在有限标记数据下进行智能轴承故障诊断。在第一阶段，提出了一种包含七个 DA 策略的 DA 方法，以扩展每种健康条件下有限标记样本的特征空间。采用交叉熵损失和三元组损失相结合的优化目标来扩大不同健康条件下有限标记样本的特征分布之间的界限。在第二阶段，采用 K-means 技术获取不同健康条件下有限标记样本的聚类中心。在第三阶段，首先根据未标记样本的特征分布与原始标记样本的各个聚类中心之间的隶属关系估计未标记样本的标签信息，然后引入 Kullback-Leibler 散度损失以最小化差异未标记样本的特征分布与其对应的聚类中心之间。在两个案例研究中评估了所提出方法的有效性，一个是来自我们实验室测试台的实验轴承故障数据集，另一个是来自轴承退化测试的公开数据集。