Wasserstein distance-based asymmetric adversarial domain adaptation in intelligent bearing fault diagnosis

Abstract

Abstract Addressing the phenomenon of data sparsity in hostile working conditions, which leads to performance degradation in traditional machine learning-based fault diagnosis methods, a novel Wasserstein distance-based asymmetric adversarial domain adaptation is proposed for unsupervised domain adaptation in bearing fault diagnosis. A generative adversarial network-based loss and asymmetric mapping are integrated to alleviate the difficulty of the training process in adversarial transfer learning, especially when the domain shift is serious. Moreover, a simplified lightweight architecture is introduced to enhance the generalization and representation capability and reduce the computational cost. Experimental results show that our method not only achieves outstanding performance with sufficient data, but also outperforms these prominent adversarial methods with limited data (both source and target domain), which provides a promising approach to real industrial bearing fault diagnosis.