Multibinary Similarity Filtering-Enabled Multisource Domain Open-Set Fault Diagnosis for Rotating Machinery Under Cross Conditions

Transfer learning has shown great promise for fault diagnosis of rotating machinery. However, in open-set scenarios, where unknown fault classes may exist and the conditions more closely reflect real-world settings. Therefore, fault diagnosis becomes significantly more challenging. We propose an end-to-end multibinary similarity filtering-based multisource domain transfer learning (MBSF-MDTL) method for cross-condition open-set fault diagnosis. This method aims to overcome two major challenges in current open-set fault diagnosis: poor cross-condition transferability and the lack of effective multisource evaluation mechanisms. MBSF-MDTL constructs multiple subtransfer networks, each handling a source–target domain pair. Within each subnetwork, adversarial learning is employed for domain alignment, while the multibinary similarity filtering (MBSF) strategy is used to distinguish known and unknown classes, enabling the training of a dedicated classifier for the target domain. In addition, a fusion decision module (FDM) integrates diagnostic results from multiple sources using various metrics to produce the optimal fault identification outcome. Extensive evaluations, including effectiveness analysis, comparative experiments, comparisons between multisource and mixed-source domains, and ablation studies, confirm the robustness and effectiveness of the proposed MBSF-MDTL method for open-set transfer learning fault diagnosis. The average accuracy across 15 transfer tasks is 84.44%, with an average H-score of 80.81%.