Deep multi feature dynamic adversarial diagnosis approach of rotating machinery

Abstract

Recent works show that knowledge transfer is an effective strategy to solve cross-domain diagnosis problems. The existing domain adaptation methods considering both global and local distribution between domains do not make the most of the knowledge learned by deep neural network, resulting in low diagnosis accuracy. To solve this problem, a deep multi feature dynamic adversarial diagnosis (DMDAD) method for transfer diagnosis of rotating machinery is presented in this paper. Firstly, the one-dimensional deep convolutional neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The class prediction vector and feature vector are fused by the multilinear mapping. The fused features are conducted for the dynamic discrimination network for adversarial training. At the same time, considering the statistical alignment and adversarial alignment, the domain adaptation is finally realized. The experimental study demonstrates the effectiveness of the DMDAD.