Domain adaptation indeed promotes the progress of intelligent fault diagnosis in industrial scenarios. The abundant labeled samples are not necessary. The identical distribution between the training and testing datasets is not any more the prerequisite for intelligent fault diagnosis working. However, two issues arise subsequently: Feature learning in domain adaptation framework tends to be biased to the source domain, and unreliable pseudolabeling seriously impacts on the conditional domain adaptation. In this article, a new domain adaptation approach with self-supervised learning and feature clustering (DASSL-FC) is proposed, trying to alleviate the issues by unbiased feature learning and pseudolabels updating strategy. Taking different transformation methods as pretext, the transformed data and its pretext train a neural network in an SSL way. As to pseudolabeling, clusters are taken as the auxiliary information to correct the network predicted labels in terms of the “strong cluster” rule. Then, the updated pseudolabels and their confidence are enforced to further estimate the conditional distribution discrepancy and its confidence weight. To verify the effectiveness of the proposed method, the experiments are implemented on intraplatform and interplatforms for simulating the practical scenarios.

中文翻译：

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具有自监督学习和特征聚类的领域适应智能故障诊断


领域适配确实促进了工业场景下智能故障诊断的进步。不需要大量的标记样品。训练数据集和测试数据集之间的相同分布不再是智能故障诊断工作的先决条件。然而，随后出现了两个问题：域适应框架中的特征学习往往会偏向源域，并且不可靠的伪标签严重影响条件域适应。在本文中，提出了一种具有自监督学习和特征聚类的新领域适应方法（DASSL-FC），试图通过无偏特征学习和伪标签更新策略来缓解该问题。以不同的变换方法为借口，变换后的数据及其借口以SSL方式训练神经网络。对于伪标签，以簇作为辅助信息，根据“强簇”规则来校正网络预测标签。然后，强制更新后的伪标签及其置信度，以进一步估计条件分布差异及其置信度权重。为了验证所提方法的有效性，在平台内和平台间进行了实验，模拟实际场景。