Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
This addresses the challenge of applying deep learning models to different operating conditions in fault diagnosis, where labeled data is scarce, but it is incremental as it builds on existing UDA methods with a focus on calibration.
The paper tackles the problem of poorly calibrated confidence estimates in unsupervised domain adaptation for intelligent fault diagnosis, which limits pseudo-label quality and leads to error accumulation, by proposing the Calibrated Adaptive Teacher method that calibrates teacher network predictions during self-training, achieving state-of-the-art performance on most transfer tasks in experiments on the Paderborn benchmark.
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.