Energy-Based Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Electro-Mechanical Systems

Recently, fault diagnosis methods based on deep learning for electro-mechanical systems have achieved excellent classification performance when the training and test data satisfied independent and identical distributions. However, distribution inconsistencies frequently occur due to operating condition variations. As a promising tool for cross-domain fault diagnosis, transfer learning can exploit source domain knowledge to facilitate target domain learning. To implement cross-domain fault diagnosis for electro-mechanical systems, an energy-based adversarial transfer network is proposed in this paper to reduce the distribution discrepancies among different domain features by incorporating an energy-based discrimination strategy into the adversarial transfer learning mechanism, which contains a state recognition module, a discrepancy metric module, and a domain discrimination module. A feature extractor is designed in the state recognition module to provide effective spatio-temporal feature representations for different domains, and an unsupervised loss is constructed according to the entropy minimization principle to improve the recognition capability of the classifier for data samples in the target domain. In the discrepancy metric module, feature distribution alignment is achieved by minimizing the distribution differences between the source domain features and the target domain ones. Furthermore, an energy discriminator is developed to effectively drive adversarial learning by introducing a flexible energy function instead of an explicit probability calculation for distinguishing the feature distributions of different domains. Two bearing datasets and one circuit dataset derived from different test platforms with various loads, speeds, stresses and fault degrees are investigated to verify the merits of the proposed model under variable operating conditions. The experimental results indicate that the proposal is superior to other state-of-the-art methods.