Time-frequency methods for structural health monitoring

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 3; pp. 5147 - 5173
• Main Authors: Pyayt, Alexander L, Kozionov, Alexey P, Mokhov, Ilya I, Lang, Bernhard, Meijer, Robert J, Krzhizhanovskaya, Valeria V, Sloot, Peter M A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.03.2014; MDPI
• Subjects: Algorithms; Anomalies; anomaly detection; Cities; Concrete; Creeks & streams; Dams; Electronic mail systems; Failure; Feature extraction; Flood control; flood protection systems; Floods; Fourier Analysis; Health monitoring (engineering); leakage detection; levee monitoring; Levees; Levees & battures; one-side classification; Phase shift; Porosity; Pressure; Rivers; Sensors; Structural health monitoring; Structure Collapse; Time Factors; time-frequency analysis; Wavelet Analysis; Netherlands; Germany; Russia; Europe, Rhine R; British Isles; USA, Massachusetts, Boston
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140305147

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).