期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 90, 期 -, 页码 64-78出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.12.002
关键词
Structural health monitoring; Data normalization; Novelty detection; Bridge engineering; Adaptive kernel spectral clustering
资金
- European Research Council under European Union/ERC AdG A-DATADRIVE-B [290923]
- Research Council KUL: CoE [PFV/10/002, BIL12/11T]
- PhD/Postdoc grants Flemish Government: FWO [G.0377.12, G.088114N]
- PhD/Postdoc grant iMinds Medical Information Technologies [SBO 2015 IWT: POM II SBO 100031]
- Belgian Federal Science Policy Office [IUAP P7/19]
- Research Foundation Flanders (FWO), Belgium
- European Research Council (ERC) [290923] Funding Source: European Research Council (ERC)
Structural health monitoring refers to the process of measuring damage-sensitive variables to assess the functionality of a structure. In principle, vibration data can capture the dynamics of the structure and reveal possible failures, but environmental and operational variability can mask this information. Thus, an effective outlier detection algorithm can be applied only after having performed data normalization (i.e. filtering) to eliminate external influences. Instead, in this article we propose a technique which unifies the data normalization and damage detection steps. The proposed algorithm, called adaptive kernel spectral clustering (AMC), is initialized and calibrated in a phase when the structure is undamaged. The calibration process is crucial to ensure detection of early damage and minimize the number of false alarms. After the calibration, the method can automatically identify new regimes which may be associated with possible faults. These regimes are discovered by means of two complementary damage (i.e. outlier) indicators. The proposed strategy is validated with a simulated example and with real-life natural frequency data from the Z24 pre-stressed concrete bridge, which was progressively damaged at the end of a one-year monitoring period. (C) 2016 Elsevier Ltd. All rights reserved.
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