TY  - JOUR
A1  - Alkam, Feras
A1  - Lahmer, Tom
T1  - Eigenfrequency-Based Bayesian Approach for Damage Identification in Catenary Poles
JF  - Infrastructures
N2  - 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 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.
KW  - Fahrleitung
KW  - Schaden
KW  - Fahrleitungsmast
KW  - Schadenserkennung
KW  - vibration-based damage identification
KW  - Bayesian inference
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210510-44256
UR  - https://www.mdpi.com/2412-3811/6/4/57
VL  - 2021
IS  - Volume 6, issue 4, article 57
SP  - 1
EP  - 19
PB  - MDPI
CY  - Basel
ER  -