Cargando…
Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection
With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255421/ https://www.ncbi.nlm.nih.gov/pubmed/37299785 http://dx.doi.org/10.3390/s23115058 |
_version_ | 1785056867746054144 |
---|---|
author | Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng |
author_facet | Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng |
author_sort | Li, Xiaofei |
collection | PubMed |
description | With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy. |
format | Online Article Text |
id | pubmed-10255421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102554212023-06-10 Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng Sensors (Basel) Article With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy. MDPI 2023-05-25 /pmc/articles/PMC10255421/ /pubmed/37299785 http://dx.doi.org/10.3390/s23115058 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Xiaofei Guo, Hainan Xu, Langxing Xing, Zezheng Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title | Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title_full | Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title_fullStr | Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title_full_unstemmed | Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title_short | Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection |
title_sort | bayesian-based hyperparameter optimization of 1d-cnn for structural anomaly detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255421/ https://www.ncbi.nlm.nih.gov/pubmed/37299785 http://dx.doi.org/10.3390/s23115058 |
work_keys_str_mv | AT lixiaofei bayesianbasedhyperparameteroptimizationof1dcnnforstructuralanomalydetection AT guohainan bayesianbasedhyperparameteroptimizationof1dcnnforstructuralanomalydetection AT xulangxing bayesianbasedhyperparameteroptimizationof1dcnnforstructuralanomalydetection AT xingzezheng bayesianbasedhyperparameteroptimizationof1dcnnforstructuralanomalydetection |