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Traditional intelligent fault diagnosis of rolling bearings work well onlyunder a common assumption that the labeled training data (source domain) andunlabeled 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 adversarialadaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNNconsists of four parts, namely, a source feature extractor, a target featureextractor, a label classifier and a domain discriminator. The layers betweenthe source and target feature extractor are partially untied during thetraining stage to take both training efficiency and domain adaptation intoconsideration. Experiments show that A2CNN has strong fault-discriminative anddomain-invariant capacity, and therefore can achieve high accuracy underdifferent working conditions. We also visualize the learned features and thenetworks to explore the reasons behind the high performance of our proposedmodel.

Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition