Failure Severity Assessment of Reactor Coolant Pump Motor Shielding Sleeves Based on Multi-Source Domain-Adaptive Adversarial Transfer Learning

Accurate assessment of shielding sleeve failure severity is essential for ensuring the safe and reliable operation of reactor coolant pump motors. However, local shielding sleeve failures induce only weak variations in conventional motor operating characteristics, while strict safety constraints make it difficult to obtain sufficient actual failure samples. Moreover, distribution discrepancies among different data domains further limit the assessment accuracy and generalization capability of conventional data-driven methods. To address these issues, this paper proposes a shielding sleeve failure severity assessment method based on multi-source domain-adaptive adversarial transfer learning. First, parametric finite-element models of shielding sleeve bulging and wear are established. By analyzing the sensitivity of different operating characteristics to shielding sleeve degradation, eddy current loss is selected as the failure-sensitive indicator, and mechanism-based mapping models between eddy current loss and the bulging displacement and wear depth are derived. Second, a multi-source domain-adaptive adversarial transfer network is developed. A shared–private feature extraction architecture is employed to jointly represent common degradation information and source-specific characteristics, while domain-adversarial learning is used to reduce the feature-distribution discrepancy between the source and target domains. The prediction results from multiple source domains are further adaptively fused according to their distribution relevance to the target domain. Finally, the proposed method is validated using both simulation and prototype experimental data. The prediction accuracy exceeds 96.8% for all simulation-based transfer tasks, with an overall improvement of 8.6% compared with the single-source adversarial transfer model. When experimental data are used as the target domain for simulation-to-experiment transfer, the prediction accuracy reaches 94.7%. These results demonstrate the effectiveness and cross-domain generalization capability of the proposed method for shielding sleeve failure severity assessment under limited target-domain labeling conditions.