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arXiv · 2606.18882

Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems

Bernardo Feijó Junqueira·Claudio Kiyoshi Umezu·Bruno Bilhar Karaziack·Tomaz Junior·Daniel Alves Castello
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Abstract

This work investigates the application of a domain-shift aware neural network for regression tasks aimed at estimating unbalance masses in rotating shafts under varying operating conditions. Experimental data were collected from a test rig in which a primary shaft, equipped with a flange carrying unbalanced masses, was driven at different rotational speeds, while a secondary shaft could be optionally activated to introduce domain discrepancy. The unbalance masses were positioned at a fixed radial distance, and the dynamic response of the system was recorded using triaxial accelerometers. The inverse problem of mass estimation is formulated within a domain adaptation framework, where the network is trained with a maximum mean discrepancy strategy to align feature representations across source and target distributions. The results demonstrate the effectiveness of explicitly addressing domain shift in improving prediction accuracy, especially when the system's physical behavior and sources of domain discrepancy are not fully known and fall outside the training conditions. These findings highlight the potential of domain-shift aware models for regression tasks in Structural Health Monitoring.

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Bernardo Feijó Junqueira, Claudio Kiyoshi Umezu, Bruno Bilhar Karaziack, Tomaz Junior, Daniel Alves Castello. 2026-06-17. Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems. https://arxiv.org/abs/2606.18882

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