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Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles

  • This study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches describedThis study proposes an efficient Bayesian, frequency-based damage identification approach to identify damages in cantilever structures with an acceptable error rate, even at high noise levels. The catenary poles of electric high-speed train systems were selected as a realistic case study to cover the objectives of this study. Compared to other frequency-based damage detection approaches described in the literature, the proposed approach is efficiently able to detect damages in cantilever structures to higher levels of damage detection, namely identifying both the damage location and severity using a low-cost structural health monitoring (SHM) system with a limited number of sensors; for example, accelerometers. The integration of Bayesian inference, as a stochastic framework, in the proposed approach, makes it possible to utilize the benefit of data fusion in merging the informative data from multiple damage features, which increases the quality and accuracy of the results. The findings provide the decision-maker with the information required to manage the maintenance, repair, or replacement procedures.show moreshow less

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Metadaten
Document Type:Article
Author: Feras AlkamORCiD, Prof. Dr. rer. nat. Tom LahmerORCiDGND
DOI (Cite-Link):https://doi.org/10.3390/infrastructures6040057Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210510-44256Cite-Link
URL:https://www.mdpi.com/2412-3811/6/4/57
Parent Title (English):Infrastructures
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2021/05/07
Date of first Publication:2021/04/13
Release Date:2021/05/10
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2021
Issue:Volume 6, issue 4, article 57
Pagenumber:19
First Page:1
Last Page:19
Tag:Fahrleitungsmast; Schadenserkennung
Bayesian inference; vibration-based damage identification
GND Keyword:Fahrleitung; Schaden
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
BKL-Classification:50 Technik allgemein / 50.16 Technische Zuverlässigkeit, Instandhaltung
56 Bauwesen / 56.03 Methoden im Bauingenieurwesen
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)