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

Understand

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.