{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:59:15Z","timestamp":1773827955061,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2014,3,12]],"date-time":"2014-03-12T00:00:00Z","timestamp":1394582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u201cstrange\u201d behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany).<\/jats:p>","DOI":"10.3390\/s140305147","type":"journal-article","created":{"date-parts":[[2014,3,12]],"date-time":"2014-03-12T07:14:58Z","timestamp":1394608498000},"page":"5147-5173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Time-Frequency Methods for Structural Health Monitoring"],"prefix":"10.3390","volume":"14","author":[{"given":"Alexander","family":"Pyayt","sequence":"first","affiliation":[{"name":"Siemens LLC, Corporate Technology, Volynskiy lane 3A, St. Petersburg, 191186, Russia"},{"name":"University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands"}]},{"given":"Alexey","family":"Kozionov","sequence":"additional","affiliation":[{"name":"Siemens LLC, Corporate Technology, Volynskiy lane 3A, St. Petersburg, 191186, Russia"},{"name":"St. Petersburg State University of Aerospace Instrumentation, Bolshaya Morskaia 67,  St. Petersburg, 190000, Russia"}]},{"given":"Ilya","family":"Mokhov","sequence":"additional","affiliation":[{"name":"Siemens LLC, Corporate Technology, Volynskiy lane 3A, St. Petersburg, 191186, Russia"}]},{"given":"Bernhard","family":"Lang","sequence":"additional","affiliation":[{"name":"Siemens AG, Corporate Technology, Muenchen, 80200, Germany"}]},{"given":"Robert","family":"Meijer","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands"},{"name":"Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO),  Eemsgolaan 3 NL-9727 DW, Groningen, The Netherlands"}]},{"given":"Valeria","family":"Krzhizhanovskaya","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands"},{"name":"National Research University ITMO, St. Petersburg, 197101, Russia"},{"name":"St. Petersburg State Polytechnic University, St. Petersburg, 195251, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3848-5395","authenticated-orcid":false,"given":"Peter","family":"Sloot","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands"},{"name":"National Research University ITMO, St. Petersburg, 197101, Russia"},{"name":"Nanyang Technological University, 639798, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2014,3,12]]},"reference":[{"key":"ref_1","unstructured":"EM-DAT: The OFDA\/CRED International Disaster Database: Flood\u2014Data and Statistics. 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