Domain generalization network based on inter-domain multivariate linearization for intelligent fault diagnosis

Abstract

Intelligent fault diagnosis technology determines the safety and reliability of equipment operation, and domain-based adaptive fault diagnosis models have been explored for solving the problem of data distribution discrepancies caused by different operating conditions. However, the requirement of obtaining the unlabeled target domain data in advance limits its application in real-world equipment operating scenarios. To address this problem, this paper proposes an inter-domain multivariate linearization (IML)-guided domain generalization network (IMLNet) for intelligent fault diagnosis. A domain multivariate fusion generation module is designed to construct new domains by linearizing between different domains using inter-domain multivariate linearization, which helps the network to extract domain invariant features in depth. Meanwhile, by fusing the multi-attention mechanism and feature pyramid network on the basis of residual network, it promotes the network to capture multi-scale information and provide richer semantic information. The effectiveness of the method is verified through two different fault diagnosis cases.