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（IFD）已被证明是一种有效且灵活的解决方案，吸引了广泛研究。深度神经网络能够从大量代表性标注数据中学习丰富表征以应用于各类任务。在IFD中，这些网络能够以端到端方式从信号中实现高分类性能，且无需大量领域知识。然而，深度学习模型通常仅在其训练数据分布上表现良好。当应用于不同分布时，其性能可能下降。这种现象在IFD中同样存在——资产往往在与标注数据采集工况不同的条件下运行。无监督域自适应（UDA）处理此类场景：源域中存在标注数据，而目标域仅有未标注数据，其中域可能对应不同的运行工况。当前方法依赖利用目标样本的置信伪标签进行训练。然而，基于置信度的伪标签选取因目标域中置信度估计校准不良而受阻——这主要源于过度自信的预测——从而限制了伪标签质量并导致误差累积。本文提出一种名为校准自适应教师（CAT）的新型UDA方法，该方法在自训练过程中利用后验校准技术对教师网络的预测进行校准。我们在域自适应IFD任务上评估CAT，并在帕德博恩轴承故障诊断基准上开展变工况下的广泛实验。我们提出的方法在大多数迁移任务中取得了最优性能。