A domain-shift aware neural network is proposed for estimating unbalance masses in rotating shafts under varying operating conditions. The model uses maximum mean discrepancy to align feature representations across different operational domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications where domain discrepancies are unknown or unaccounted for.