Time-frequency Methods for Structural Health Monitoring

Autor: Valeria V. Krzhizhanovskaya, Bernhard Lang, Alexey P. Kozionov, Ilya I. Mokhov, Robert Meijer, Alexander L. Pyayt, Peter M. A. Sloot
Přispěvatelé: Computational Science Lab (IVI, FNWI), System and Network Engineering (IVI, FNWI)
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Engineering
Communication & Information
Time Factors
Classification technique
Anomaly detection
Flood protection systems
Wavelet analysis
Information Society
BIS - Business Information Services
lcsh:Chemical technology
sensors
anomaly detection
structural health monitoring
time-frequency analysis
flood protection systems
levee monitoring
one-side classification
leakage detection
Biochemistry
Civil engineering
Analytical Chemistry
Wavelet
lcsh:TP1-1185
Instrumentation
Structure Collapse
TS - Technical Sciences
geography.geographical_feature_category
Warning system
Fourier Analysis
Infrastructures
One-side classification
Atomic and Molecular Physics
and Optics
Flood control
Time-frequency analysis
Feature extraction
Structural health monitoring
Levee
Porosity
Algorithms
Levee monitoring
TP1-1185
Article
Rivers
Pressure
Electrical and Electronic Engineering
Cities
geography
Flood myth
business.industry
Sensors
Chemical technology
Floods
13. Climate action
Leakage detection
business
Zdroj: Sensors, Vol 14, Iss 3, Pp 5147-5173 (2014)
Issue 3
Pages 5147-5173
Sensors
Sensors (Basel, Switzerland)
Volume 14
Sensors, 14(3), 5147-5173
NARCIS
Multidisciplinary Digital Publishing Institute
OpenAIRE
DOAJ-Articles
Europe PubMed Central
Sensors, 14(3), 5147-5173. Multidisciplinary Digital Publishing Institute (MDPI)
Sensors (Switzerland), 3, 14, 5147-5173
Sensors; Volume 14; Issue 3; Pages: 5147-5173
ISSN: 1424-8220
DOI: 10.3390/s140305147
Popis: Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany) and "strange" behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany). © 2014 by the authors; licensee MDPI, Basel, Switzerland.
Databáze: OpenAIRE
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