We test whether a representation learned from interferometric strain transients in gravitational-wave observatories can act as a frozen morphology-sensitive operator for unseen sensors, provided the target signals preserve coherent elastic transient structure. Using a neural encoder trained exclusively on non-Gaussian instrumental glitches, we perform strict zero-shot anomaly analysis on rolling-element bearings without retraining, fine-tuning, or target-domain labels. On the IMS-NASA run-to-failure dataset, the operator yields a monotonic health index HI(t) = s0.99(t)/tau normalized to an early-life reference distribution, enabling fixed false-alarm monitoring at 1-q = 1e-3 with tau = Q0.999(P0). In discrete fault regimes (CWRU), it achieves strong window-level discrimination (AUC_win about 0.90) and file-level separability approaching unity (AUC_file about 0.99). Electrically dominated vibration signals (VSB) show weak, non-selective behavior, delineating a physical boundary for transfer. Under a matched IMS controlled-split protocol, a generic EfficientNet-B0 encoder pretrained on ImageNet collapses in the intermittent regime (Lambda_tail about 2), while the interferometric operator retains strong extreme-event selectivity (Lambda_tail about 860), indicating that the effect is not a generic property of CNN features. Controlled morphology-destruction transformations selectively degrade performance despite per-window normalization, consistent with sensitivity to coherent time-frequency organization rather than marginal amplitude statistics.

翻译：我们测试从引力波观测站的干涉应变瞬态信号中学习到的表征，是否能够作为针对未见传感器的冻结形态敏感算子，前提是目标信号保持相干的弹性瞬态结构。利用一个仅使用非高斯仪器毛刺训练得到的神经编码器，我们在滚动轴承上执行严格的零样本异常分析，无需重新训练、微调或目标域标签。在IMS-NASA运行至失效数据集上，该算子产生一个单调的健康指数HI(t) = s0.99(t)/tau，该指数归一化到早期寿命参考分布，从而能够在固定虚警率1-q = 1e-3下进行监测，其中tau = Q0.999(P0)。在离散故障机制数据集（CWRU）中，它实现了强大的窗口级判别能力（AUC_win约0.90）和接近完美的文件级可分离性（AUC_file约0.99）。电主导的振动信号（VSB）表现出微弱且非选择性的行为，划定了迁移的物理边界。在匹配的IMS受控分割协议下，一个在ImageNet上预训练的通用EfficientNet-B0编码器在间歇性机制中失效（Lambda_tail约2），而干涉算子则保持了强大的极端事件选择性（Lambda_tail约860），表明该效应并非CNN特征的通用属性。尽管进行了逐窗口归一化，受控的形态破坏变换仍选择性地降低了性能，这与对相干时频组织而非边缘幅度统计的敏感性相一致。