• • MCOAN replaces the fixed empirical threshold of 0.5 used in conventional open-set back-propagation (OSBP) adversarial loss with per-sample dynamic thresholds derived from dual K-way and K+1-way classifier similarity scores, eliminating the subjective prior threshold setting that causes negative transfer when unknown-class proportions vary across wind farm sites.
• • The non-adversarial domain classifier stabilizes dynamic weight computation, preventing the weight oscillation that degrades known-class alignment when adversarial gradients from unknown samples dominate the shared feature space under variable speed and load conditions.
• • Validation across two datasets confirms high-precision shared-class feature distribution alignment and unknown-class recognition, with robustness maintained under the distribution shift regime characteristic of wind turbine drivetrains operating at variable rotational speeds and torque loads.
• • The architecture addresses the closed-set assumption failure documented in domain adversarial neural networks (DANN) and statistical moment matching methods, which misclassify unknown bearing fault modes as known classes and induce negative transfer when the entire target domain is blindly aligned to the source domain.