Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation

•A new multi-target domain fault diagnostic method is proposed.•An attention mechanism adaptively assigns weights to data from multiple sensors.•Classification feature augmentation is combined with subdomain adaptation for aligning feature distributions.•Domain invariant feature augmentation is combined with domain adversarial for finding real domain invariant features. Intelligent diagnostic methods for identifying faults in rotating machinery, based on domain adaptation, have garnered significant attention. However, most current domain adaptation approaches are primarily designed for single-source domain and single-target domain (SSST) applications. There is a dearth of domain adaptation approaches tailored for single-source to multi-target domains (SSMT). In contrast to SSST, SSMT takes a more comprehensive approach by considering relationships across multiple target domains. This approach offers increased versatility and a broader range of potential applications. To address this, an end-to-end multi-target adversarial subdomain adaptation method is proposed that leverages attention mechanism data fusion and mixup feature augmentation. Firstly, the attention mechanism is used to fuse data from different sensors in both channel and spatial dimensions. Subsequently, a mixup-based feature augmentation method is proposed for multi-target domain adaptation. The method is combined with subdomain adaptation and domain discrimination to further reduce the distributional differences between the source and various target domains while relieving the overfitting problem during domain adaptation. Finally, with the above approach, a robust and stable model for multiple target domain fault diagnosis can be trained. Our experimental results illustrate that our approach has higher accuracy and robustness compared to several popular domain adaptation methods.