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Lost data neural semantic recovery framework for structural health monitoring based on deep learning
Computer-Aided Civil and Infrastructure Engineering ( IF 9.6 ) Pub Date : 2022-05-05 , DOI: 10.1111/mice.12850
Kejie Jiang 1 , Qiang Han 1 , Xiuli Du 1
Affiliation  

Structural condition perception is a crucial step in structural health monitoring (SHM). Random loss or corruption of sensing data seriously hinders the reliability of the monitoring system. This paper discusses the recovery of randomly lost data in SHM from the perspective of conditional probability generation. A novel data-driven neural semantic recovery framework is proposed, transforming data recovery into a conditional probability modeling problem. This framework uses deep fully convolutional neural networks with an encoder–decoder architecture to capture the overall semantic features of the vibration data, allowing accurate modeling of the behavior of complex conditional probability distributions. Advanced techniques such as dense connections, skip connections, and residual connections significantly improved the network's parameter utilization and recovery performance. Moreover, a novel perceptual loss function is proposed, enabling the network to integrate data loss patterns effectively. The proposed network can be trained end-to-end in a self-supervised manner and perform efficient inferences. Based on the long-term measured acceleration response under the ambient excitation of a pedestrian bridge, the recovery performance and robustness of the model are sufficiently verified and evaluated. The network exhibits excellent recovery accuracy and robustness, even if the loss ratio is as high as 90%. Preliminary evaluation results show that the proposed model can be seamlessly transferred to scenarios with continuous data loss without retraining the network. Finally, the application prospects of the framework in modal identification and anomaly monitoring of structural conditions are demonstrated.

中文翻译:

基于深度学习的结构健康监测丢失数据神经语义恢复框架

结构状态感知是结构健康监测 (SHM) 的关键步骤。传感数据的随机丢失或损坏严重阻碍了监测系统的可靠性。本文从条件概率生成的角度讨论了SHM中随机丢失数据的恢复。提出了一种新的数据驱动的神经语义恢复框架,将数据恢复转化为条件概率建模问题。该框架使用具有编码器-解码器架构的深度全卷积神经网络来捕获振动数据的整体语义特征,从而可以对复杂条件概率分布的行为进行准确建模。密集连接、跳过连接和残差连接等先进技术显着改善了网络 s 参数利用和恢复性能。此外,提出了一种新颖的感知损失函数,使网络能够有效地整合数据丢失模式。所提出的网络可以以自我监督的方式进行端到端的训练并执行有效的推理。基于人行天桥环境激励下的长期实测加速度响应,对模型的恢复性能和鲁棒性进行了充分验证和评价。即使丢失率高达 90%,该网络也表现出出色的恢复精度和鲁棒性。初步评估结果表明,所提出的模型可以无缝转移到数据连续丢失的场景,而无需重新训练网络。最后,
更新日期:2022-05-06
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