Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage

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Cahill, Paul
Hazra, Budhaditya
Karoumi, Raid
Mathewson, Alan
Pakrashi, Vikram
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Elsevier Ltd.
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The application of energy harvesting technology for monitoring civil infrastructure is a bourgeoning topic of interest. The ability of kinetic energy harvesters to scavenge ambient vibration energy can be useful for large civil infrastructure under operational conditions, particularly for bridge structures. The experimental integration of such harvesters with full scale structures and the subsequent use of the harvested energy directly for the purposes of structural health monitoring shows promise. This paper presents the first experimental deployment of piezoelectric vibration energy harvesting devices for monitoring a full-scale bridge undergoing forced dynamic vibrations under operational conditions using energy harvesting signatures against time. The calibration of the harvesters is presented, along with details of the host bridge structure and the dynamic assessment procedures. The measured responses of the harvesters from the tests are presented and the use the harvesters for the purposes of structural health monitoring (SHM) is investigated using empirical mode decomposition analysis, following a bespoke data cleaning approach. Finally, the use of sequential Karhunen Loeve transforms to detect train passages during the dynamic assessment is presented. This study is expected to further develop interest in energy-harvesting based monitoring of large infrastructure for both research and commercial purposes.
Energy harvesting , Bridge structure , Full-scale testing , Structural health monitoring , Empirical mode decomposition , Scalogram , Sequential Karhunen Loeve transform , Hilbert transform
Cahill, P., Hazra, B., Karoumi, R., Mathewson, A. and Pakrashi, V. (2018) 'Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage', Mechanical Systems and Signal Processing, 106, pp. 265-283. doi: 10.1016/j.ymssp.2018.01.007