Novel dual-network autoencoder based adversarial domain adaptation with Wasserstein divergence for fault diagnosis of unlabeled data

Abstract

Intelligent fault diagnostic techniques based on deep learning have been developed by leaps and bounds, but it is quite difficult to construct a good fault diagnosis model without obtaining sufficient fault data labels. To solve this problem, this article proposes a novel dual-network autoencoder based adversarial domain adaptation with Wasserstein divergence (DWADA). Firstly, a dual-network autoencoder composed of the convolutional neural network (CNN) and the long short-term memory (LSTM) is regarded as a feature extractor for extracting local deep features and adding temporal feature information, while unsupervised reconstruction of the source domain data can ensure high accuracy of the classifier. Secondly, the domain discriminator forms an adversarial training with the feature extractor to facilitate the feature extractor to extract domain-invariant features for classification by minimizing the Wasserstein distance that measures the difference in feature distribution between different domains. Finally, Wasserstein divergence is introduced to the adversarial process to remove the k-Lipschitz constraint for improving the stability of the training. The Tennessee Eastman process (TEP) and the industrial three-phase flow process (TPFP) are applied to verify the performance of DWADA. Simulation results show that DWADA outperforms other related methods in transfer fault diagnosis tasks under different operating conditions.