用域偏移感知模型提升旋转系统不平衡质量预测精度
Domain-Shift Aware Neural Networks for Unbalance Characterization in Rotating Systems
- 构建域适应神经网络，通过最大均值差异对齐特征分布
- 在不同转速与干扰条件下，预测误差显著降低
- 适合结构健康监测中复杂工况下的不平衡诊断
本文研究了域偏移感知神经网络在旋转轴不平衡质量回归估计中的应用。实验数据来自一台试验台，主轴装有固定径向位置的不平衡质量块，在不同转速下运行，辅轴可选激活以引入域差异。系统动态响应由三轴加速度计记录。将质量估计逆问题置于域适应框架中，采用最大均值差异策略训练网络，实现源域与目标域特征表示的对齐。结果表明，显式处理域偏移能有效提升预测精度，尤其在系统物理行为和域偏移来源未知且超出训练条件时表现更优。这些发现凸显了域偏移感知模型在结构健康监测回归任务中的潜力。
原文摘要 · Abstract (English)
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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