  Traditional intelligent fault diagnosis of rolling bearings work well only
under a common assumption that the labeled training data (source domain) and
unlabeled testing data (target domain) are drawn from the same distribution.
However, in many real-world applications, this assumption does not hold,
especially when the working condition varies. In this paper, a new adversarial
adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN
consists of four parts, namely, a source feature extractor, a target feature
extractor, a label classifier and a domain discriminator. The layers between
the source and target feature extractor are partially untied during the
training stage to take both training efficiency and domain adaptation into
consideration. Experiments show that A2CNN has strong fault-discriminative and
domain-invariant capacity, and therefore can achieve high accuracy under
different working conditions. We also visualize the learned features and the
networks to explore the reasons behind the high performance of our proposed
model.
