领域自适应何时对物理振动传感器有帮助？一项关于神经算子与卷积模型的留出轴承研究
When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models
AI总结：
本研究通过留出轴承协议评估物理振动传感器上的领域自适应，发现输入表示（阶次域）比对齐方法更关键，傅里叶神经算子结合阶次跟踪显著提升迁移性能。
AI中文摘要：
从振动信号中诊断滚动轴承故障是一项典型的物理传感任务，也是操作条件变化下领域自适应的广泛使用的基准。通常报告的准确率超过99%，但这些结果是在将同一物理轴承同时置于训练集和测试集的评估划分下获得的。我们在留出轴承协议下重新审视该任务，将每个轴承单元完全分配给训练集或测试集，发现仅源域迁移的性能远低于这些数字所暗示的水平：在轴速变化时，其准确率仅为0.36，而目标监督的上限为0.97。随后，我们研究了决定迁移效果的因素。将计算阶次跟踪（一种轴角重采样，使故障频率位于固定的轴阶次，与运行速度无关）视为一种受控的表示变化，我们发现傅里叶神经算子将仅源域迁移在速度变化（故障峰值移动）下的准确率从0.36提升至0.61，而特征维度匹配的卷积网络在两种表示下均接近随机水平。表示还决定了无监督对齐是否有效：使用相同的归一化RBF-MMD损失且无目标标签时，算子分别在频域达到0.71，在阶次域达到0.95，与目标监督上限相差0.02以内，且在每一个留出轴承折上均高于0.86。一旦表示正确，少量标签预算带来的提升微乎其微。这些结果表明，对于该任务，输入表示而非对齐方法决定了自适应是否有帮助。第二个数据集（其留出单元为故障直径而非轴承）显示，相同的协议暴露了即使目标监督模型也无法避免的失败。
英文摘要：
Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under operating-condition shift. Accuracies above 99 percent are commonly reported, but under evaluation splits that place the same physical bearing in both training and test. We revisit the task under a held-out-bearing protocol, assigning every bearing unit entirely to either the training or the test set, and find that source-only transfer is far weaker than such numbers suggest: on a change of shaft speed it reaches only $0.36$, against a target-supervised ceiling of 0.97. We then study what governs transfer. Treating computed order tracking, a shaft-angle resampling that places fault frequencies at fixed shaft orders independent of running speed, as a controlled change of representation, we find that a Fourier Neural Operator raises source-only transfer from $0.36$ to $0.61$ on the speed shift, where the fault peaks move, while a convolutional network of matched feature dimension stays near chance in both representations. The representation also decides whether unsupervised alignment can work: with the same normalized RBF-MMD loss and no target labels, the operator reaches 0.71 in the frequency domain but 0.95 in the order domain, within 0.02 of the target-supervised ceiling and above $0.86$ on every held-out bearing fold. Once the representation is right, a small label budget adds little. These results indicate that, for this task, the input representation rather than the alignment method decides whether adaptation helps. A second dataset, whose held-out units are fault diameters rather than bearings, shows that the same protocol exposes failures that even a target-supervised model cannot avoid.
