发表机构
FG Machine Dynamics and Acoustics, TH Wildau（TH Wildau 机器动力学与声学研究组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LDV成本高及早期螺栓预紧力损失信号弱的问题，提出稀疏LDV神经重建与麦克风证据晚期融合方法，在28个状态-共振对中实现100%检测率。
AI 中文摘要
激光多普勒测振（LDV）成本高昂，而早期螺栓预紧力损失可能产生微弱且依赖共振的变化。我们将以密集调试基线为参考的稀疏LDV重建与力归一化麦克风证据相结合。在m=24个测量节点处，每个单螺栓5-Nm状态由排除该状态的神经网络折叠重建。基于健康基线校准的晚期融合规则，将未测量节点处局部FRAC缺陷的第90百分位数与按冲击水平可重复性归一化的麦克风H1变化相结合。仅稀疏LDV标记了24/28个轻度状态-共振对（50%预紧力损失），声学证据标记了25/28个，融合标记了全部28/28个。所有四个LDV漏检均发生在RG3（共振组）并被声学恢复。对于可重复性归一化的声学阈值从3到10，融合保持28/28。结果支持振动-声学互补证据用于早期预紧力损失筛查，同时避免对单独采集的活动进行逐点融合。
英文摘要
Laser Doppler vibrometry (LDV) is costly, while early bolt-preload loss may produce weak and resonance-dependent changes. We combine sparse-LDV reconstruction referenced to a dense commissioning baseline with force-normalized microphone evidence. At m = 24 measurement nodes, each single-bolt 5-Nm state is reconstructed by a neural-network fold that excludes that state from training. A late-fusion rule calibrated on the healthy baseline combines the 90th percentile of local-FRAC deficits at unmeasured nodes with microphone H1 changes normalized by impact-level repeatability. Sparse LDV alone flags 24/28 mild condition-resonance pairs (50% preload loss) and acoustic evidence flags 25/28, fusion flags all 28/28. All four LDV misses occur at RG3 (resonance group) and are recovered acoustically. Fusion remains 28/28 for repeatability-normalized acoustic thresholds from 3 to 10. The result supports complementary vibro-acoustic evidence for early preload-loss screening while avoiding pointwise fusion of separately acquired campaigns.
