Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various applications. In IFD, they achieve high classification performance from signals in an end-to-end manner, without requiring extensive domain knowledge. However, deep learning models usually only perform well on the data distribution they have been trained on. When applied to a different distribution, they may experience performance drops. This is also observed in IFD, where assets are often operated in working conditions different from those in which labeled data have been collected. Unsupervised domain adaptation (UDA) deals with the scenario where labeled data are available in a source domain, and only unlabeled data are available in a target domain, where domains may correspond to operating conditions. Recent methods rely on training with confident pseudo-labels for target samples. However, the confidence-based selection of pseudo-labels is hindered by poorly calibrated confidence estimates in the target domain, primarily due to over-confident predictions, which limits the quality of pseudo-labels and leads to error accumulation. In this paper, we propose a novel UDA method called Calibrated Adaptive Teacher (CAT), where we propose to calibrate the predictions of the teacher network throughout the self-training process, leveraging post-hoc calibration techniques. We evaluate CAT on domain-adaptive IFD and perform extensive experiments on the Paderborn benchmark for bearing fault diagnosis under varying operating conditions. Our proposed method achieves state-of-the-art performance on most transfer tasks.

翻译：基于深度学习的智能故障诊断已被证明是一种有效且灵活的解决方案，吸引了广泛的研究。深度神经网络能够从大量具有代表性的标注数据中学习丰富的表示，适用于多种应用场景。在智能故障诊断中，这些网络以端到端的方式直接从信号中实现高分类性能，无需依赖大量领域知识。然而，深度学习模型通常仅在训练数据分布上表现良好。当应用于不同分布的数据时，其性能可能出现下降。这一现象在智能故障诊断中同样存在，因为设备常在不同于标注数据采集工况的条件下运行。无监督领域自适应旨在处理源域存在标注数据、而目标域仅有无标注数据的情景，其中不同域可对应不同的运行工况。现有方法主要依赖基于置信度的目标样本伪标签进行训练。然而，基于置信度的伪标签选择受限于目标域中置信度估计的校准不足——主要源于预测的过度自信——这限制了伪标签的质量并导致误差累积。本文提出一种名为校准自适应教师的新型无监督领域自适应方法，该方法通过事后校准技术，在自训练过程中持续校准教师网络的预测结果。我们在领域自适应的智能故障诊断任务上评估了所提方法，并在帕德博恩轴承故障诊断基准数据集上针对变工况场景进行了大量实验。结果表明，该方法在多数迁移任务中达到了最先进的性能水平。