A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring

Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured...

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Autores principales:	Die Liu, Yihao Bao, Yingying He, Likai Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	structural health monitoring deep learning data loss recovery empirical mode decomposition bidirectional gated recurrent unit Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d9eec81b31f14cb699fde61b4ec779f5
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id	oai:doaj.org-article:d9eec81b31f14cb699fde61b4ec779f5
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spelling	oai:doaj.org-article:d9eec81b31f14cb699fde61b4ec779f52021-11-11T15:08:55ZA Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring10.3390/app1121100722076-3417https://doaj.org/article/d9eec81b31f14cb699fde61b4ec779f52021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10072https://doaj.org/toc/2076-3417Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of the method is to predict missing data using the raw data and decomposed subsequence as the decomposed subsequence can improve the predicted accuracy. In addition, the BiGRU in the hybrid model can extract the pre-post correlations of subsequence compared with traditional artificial neural networks. Raw acceleration data collected from a three-story structure are used to evaluate the performance of the EMD-BiGRU for missing data imputation. The recovery results of measure data show that the EMD-BiGRU exhibits excellent performance from two perspectives. First, the decomposed subsequence can improve the accuracy of the BiGRU predicted model. Second, the BiGRU outperforms other machine learning algorithms because it captures more microscopic changes of measured data. The experimental analysis suggests that the change patterns of raw measured signal data are complex, and therefore it is significant to extract the features before modeling.Die LiuYihao BaoYingying HeLikai ZhangMDPI AGarticlestructural health monitoringdeep learningdata loss recoveryempirical mode decompositionbidirectional gated recurrent unitTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10072, p 10072 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	structural health monitoring deep learning data loss recovery empirical mode decomposition bidirectional gated recurrent unit Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	structural health monitoring deep learning data loss recovery empirical mode decomposition bidirectional gated recurrent unit Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Die Liu Yihao Bao Yingying He Likai Zhang A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
description	Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of the method is to predict missing data using the raw data and decomposed subsequence as the decomposed subsequence can improve the predicted accuracy. In addition, the BiGRU in the hybrid model can extract the pre-post correlations of subsequence compared with traditional artificial neural networks. Raw acceleration data collected from a three-story structure are used to evaluate the performance of the EMD-BiGRU for missing data imputation. The recovery results of measure data show that the EMD-BiGRU exhibits excellent performance from two perspectives. First, the decomposed subsequence can improve the accuracy of the BiGRU predicted model. Second, the BiGRU outperforms other machine learning algorithms because it captures more microscopic changes of measured data. The experimental analysis suggests that the change patterns of raw measured signal data are complex, and therefore it is significant to extract the features before modeling.
format	article
author	Die Liu Yihao Bao Yingying He Likai Zhang
author_facet	Die Liu Yihao Bao Yingying He Likai Zhang
author_sort	Die Liu
title	A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
title_short	A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
title_full	A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
title_fullStr	A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
title_full_unstemmed	A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
title_sort	data loss recovery technique using emd-bigru algorithm for structural health monitoring
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d9eec81b31f14cb699fde61b4ec779f5
work_keys_str_mv	AT dieliu adatalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT yihaobao adatalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT yingyinghe adatalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT likaizhang adatalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT dieliu datalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT yihaobao datalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT yingyinghe datalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring AT likaizhang datalossrecoverytechniqueusingemdbigrualgorithmforstructuralhealthmonitoring
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A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring

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