Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault
  Diagnosis
Florent Forest Olga Fink
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.