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      <title>Disentangling Damage from Operational Variability: A Label-Free Self-Supervised Representation Learning Framework for Output-Only Structural Damage Identification</title>
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      <description>本文针对结构健康监测中损伤信号易被环境与操作变异掩盖的核心挑战，提出了一种**无标签、自监督的解缠表示学习框架**。该框架采用双流自编码器架构，通过**时间序列重构损失**确保信息完整性，并利用**VICReg自监督损失**（基于假设损伤状态不变的基线期数据）强制损伤敏感表征（`z_dmg`）对操作</description>
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