Abstract
This study proposes adaptive Studentized multi-kernel maximum mean discrepancy (MMD) for cross-condition machinery fault diagnosis. To account for sampling variation in mini-batch alignment, the multi-kernel MMD objective is normalized by empirical variability and integrated with domain-specific batch normalization. A label-free plateau criterion monitors fluctuations in the alignment loss and triggers joint cosine cooling of the learning rate and alignment weight, adapting the cooling onset to the observed training trajectory. The framework is evaluated on 18 window-level transfer tasks from the Paderborn bearing and PHM2009 gearbox datasets, with five random seeds per task and a fixed final-epoch reporting rule. Mean final-epoch accuracies reach 89.32% and 73.86%, exceeding DAN-style multi-kernel alignment by 2.16 and 2.39 percentage points, respectively. Compared with the fixed-schedule Studentized variant, adaptive scheduling improves accuracy by 0.57 and 0.37 percentage points, respectively. These results support combining variability-normalized alignment with label-free adaptive scheduling for cross-condition fault diagnosis.