0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Signal Anomaly Detection of Bridge SHM System Based on Two-Stage Deep Convolutional Neural Networks

Auteur(s): ORCID (National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan, People’s Republic of China)
(School of Information Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China)
(School of Information Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China)
(School of Information Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China)
(School of Information Engineering, Wuhan University of Technology, Wuhan, People’s Republic of China)
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Engineering International, , n. 1, v. 33
Page(s): 1-10
DOI: 10.1080/10168664.2021.1983914
Abstrait: Identifying and removing anomalies of sensor signals existing in the bridge structural health monitoring (SHM) system is conductive to correctly assessing the operation status of the monitored bridge. A data augmentation strategy of first-order derivation operation and equal-length sequence segmentation was proposed to extract more abundant features of signal anomalies. To reduce the impact of redundant information in the augmented data on the training efficiency of supervised learning, based on statistical analysis and ranking importance measurement, feature dimension reduction was carried out on the augmented sample dataset. Aiming at the sample dataset after dimensionality reduction, a two-stage deep convolutional neural network model that can effectively identify different signal anomaly patterns was established. The experimental results demonstrated that the proposed method can enhance the recognition accuracy on signal anomaly patterns when comparing to the effect from direct training on the original dataset.
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1080/10168664.2021.1983914.
  • Informations
    sur cette fiche
  • Reference-ID
    10635917
  • Publié(e) le:
    30.11.2021
  • Modifié(e) le:
    08.06.2023
 
Structurae coopère avec
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine