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    Item type:Publication, Access status: without files ,
    Validation of an ultra-low-cost wireless structural health monitoring system for civil infrastructure
    (DEStech Publications, 2019-10)
    Smarsly, Kay
    ;
    Mthunzi, Everett M.
    ;
    Hahn, Oliver
    ;
    Planer, Josephine
    Structural health monitoring (SHM) is critical to ensure safety of civil infrastructure, such as bridges, railways, or pipelines. However, existing SHM techniques suffer from high investment, installation, and maintenance cost. This paper presents an ultra-low-cost and easy-to-use wireless SHM system for civil infrastructure, featuring real-time sensing, on-board data analysis, energy-efficient wireless communication, and Internet of Things (IoT) connectivity. To validate its performance, the proposed SHM system is installed at Itztal Bridge, Germany, for monitoring and analyzing quasi-static horizontal deck displacements in response to thermal loads. In parallel to the ultra-low-cost SHM system, a traditional tethered SHM system, also installed at the bridge, is used in this study for direct comparison and validation. As a result, the ultra-low-cost wireless SHM system is shown to be easy-to-install and sufficiently accurate for monitoring and analyzing the behavior of civil infrastructure under static loads, serving as a low-cost yet high-performance diagnostic tool with IoT connectivity.
    Publicationtype: Conference Paper
      46
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    Item type:Publication, Access status: without files ,
    A support vector regression-based approach towards decentralized fault diagnosis in wireless structural health monitoring systems
    (2019-10)
    Steiner, Maria
    ;
    Legatiuk, Dmitrii
    ;
    Smarsly, Kay
    The reliability of sensors in structural health monitoring (SHM) systems is affected by sensor faults that compromise the quality of monitoring, causing erroneous judgment of structural conditions. To ensure reliable operation of SHM systems, techniques for diagnosing sensor faults have been proposed. In wireless SHM systems, embedding techniques for fault diagnosis (FD) into sensor nodes is of increasing importance as sensor nodes process data on board and communicate analysis results instead of large sets of raw data. As a consequence of on-board processing, raw data is frequently unavailable and sensor faults remain undetected. In this paper, a decentralized fault diagnosis approach based on support vector regression (FD-SVR) is proposed. Due to the high accuracy of the support vector regression (SVR), which can be achieved even with relatively small data sets, the FD-SVR approach enables wireless sensor nodes autonomously self-diagnose sensor faults, enhancing the reliability of wireless SHM systems without a need for large data sets to be used for fault diagnosis. The ability of the embedded FD-SVR approach to detect and isolate sensor faults, increasing the reliability of sensors in wireless SHM systems, is validated in laboratory experiments.
    Publicationtype: Conference Paper
      43