Xin Xie, Ying Lei, Chunyan Xiang, Yixian Li, Lijun Liu
{"title":"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","authors":"Xin Xie, Ying Lei, Chunyan Xiang, Yixian Li, Lijun Liu","doi":"10.1155/stc/8419570","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":"2025 1","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8419570","citationCount":"0","resultStr":"{\"title\":\"An Enhanced Generative Adversarial Imputation Network With Unsupervised Learning for Random Missing Data Imputation of All Sensors\",\"authors\":\"Xin Xie, Ying Lei, Chunyan Xiang, Yixian Li, Lijun Liu\",\"doi\":\"10.1155/stc/8419570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":49471,\"journal\":{\"name\":\"Structural Control & Health Monitoring\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/stc/8419570\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Control & Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/stc/8419570\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/stc/8419570","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
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.
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.