基于无监督学习的全传感器随机缺失数据补全增强生成对抗补全网络

IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Xin Xie, Ying Lei, Chunyan Xiang, Yixian Li, Lijun Liu
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引用次数: 0

摘要

结构健康监测(SHM)数据是结构状态评估的重要依据。但在实际SHM中,长期监测数据不可避免地会出现数据缺失,严重影响了SHM系统的可靠性。到目前为止,已经提出了许多基于深度学习的监督数据输入方法,这些方法需要完整的传感器数据进行训练。虽然有一些关于无监督数据输入的研究,但仍然需要一些完整的传感器数据。特别是对实际SHM中可能出现的所有传感器数据不完整的无监督数据输入问题缺乏研究。因此,本文提出了一种带有无监督学习的增强型生成对抗归算网络。首先,在生成对抗输入网络框架内,建立具有编码器-解码器结构的卷积神经网络(cnn)来提取重要的高级局部特征。此外,在生成网络中嵌入了自关注机制,以全局捕获数据之间的远程依赖关系。最后,为了提高网络的参数利用率和插补性能,引入了跳变连接。通过对Dowling Hall人行桥现场监测加速度数据的不完整数据进行随机缺失数据的输入,验证了该方法的有效性。结果表明,在所有传感器均存在随机数据缺失的情况下,该方法均能在时域和频域实现较好的数据输入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Enhanced Generative Adversarial Imputation Network With Unsupervised Learning for Random Missing Data Imputation of All Sensors

An Enhanced Generative Adversarial Imputation Network With Unsupervised Learning for Random Missing Data Imputation of All Sensors

Structural health monitoring (SHM) data are crucial for structural state assessment. However, long-term monitoring data are inevitably subject to data missing in actual SHM, which seriously hinders the reliability of the SHM system. So far, many deep learning-based supervised data imputation methods have been proposed, which require complete sensor data for training. Although there are studies on unsupervised data imputation, some complete sensor data are still required. Especially, there is a lack of study on the challenging problem of unsupervised data imputation with incomplete data of all sensors, which may occur in actual SHM. Therefore, an enhanced generative adversarial imputation network with unsupervised learning is proposed in this paper for such a challenging task. First, within the generative adversarial imputation network framework, convolutional neural networks (CNNs) with an encoder–decoder architecture are established to extract significant high-level local features. Furthermore, a self-attention mechanism is embedded into the generative network to globally capture remote dependencies between data. Finally, the skip connections are incorporated to enhance the parameter utilization and imputation performance of the network. The random missing data imputation with incomplete data of the field monitoring acceleration data from the Dowling Hall footbridge is used to validate the proposed method. The results show that good data imputation in both the time and frequency domains can be achieved by the proposed method in the case of random data missing in all sensors.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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