Abstract
A primary challenge in the design of reliable and long–lasting Structural Health Monitoring (SHM) systems consists in ensuring real–time functionalities through cost–effective solutions. As such, energy–aware architectures demand the joint optimization of data sampling rates, on–board storage requirements, and communication data payloads. These requirements became particularly crucial with the development of mesoscale SHM systems, where the periodic gathering of signals from increasingly denser sensor networks made the data management task a primary issue. In the specific context of vibration–based SHM, where structural responses exhibit peculiar spectral profiles characterized by a sparse frequency content concentrated around the natural frequencies, the Compressive Sensing theory inspired compelling approaches for data collection and gathering to central processing units. The current work combines such advanced sub–Nyquist sampling procedures with a low-cost/low-power miniaturized Smart Sensor Network targeted on the extraction of vibration signals. The network is constituted by several recording nodes equipped with MEMS accelerometers and microcontrollers which are arranged in clusters, and microprocessors-based cluster heads in charge of data decompression and feature extraction for the characterization of the structural integrity.
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Notes
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Incoherence expresses to what extent two different basis are orthogonal one to the other. A good measure of incoherence is provided by the scalar product: the lower the values, the higher the two basis are orthogonal.
References
Allemang, R.J.: The modal assurance criterion-twenty years of use and abuse. Sound Vib. 37(8), 14–23 (2003)
Brincker, R., Zhang, L., Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mater. Struct. 10(3), 441 (2001)
Candes, E.J., et al.: The restricted isometry property and its implications for compressed sensing. C. R. Math. 346(9–10), 589–592 (2008)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Ibrahim, R.A.: Handbook of Structural Life Assessment. Wiley, Hoboken (2017)
Klis, R., Chatzi, E.N.: Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks. Struct. Infrastruct. Eng. 13(1), 195–209 (2017)
O’Connor, S.M., Lynch, J.P., Gilbert, A.C.: Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications. Smart Mater. Struct. 23(8), 085014 (2014)
Orović, I., Papić, V., Ioana, C., Li, X., Stanković, S.: Compressive sensing in signal processing: algorithms and transform domain formulations. Math. Prob. Eng. 2016 (2016)
Paz, M., Kim, Y.H., et al.: Structural Dynamics. Springer, Heidelberg (1991)
Sony, S., Laventure, S., Sadhu, A.: A literature review of next-generation smart sensing technology in structural health monitoring. Struct. Control Health Monit. 26(3), e2321 (2019)
Testoni, N., Aguzzi, C., Arditi, V., Zonzini, F., De Marchi, L., Marzani, A., Cinotti, T.S.: A sensor network with embedded data processing and data-to-cloud capabilities for vibration-based real-time SHM. J. Sens. (2018)
Testoni, N., Zonzini, F., Marzani, A., Scarponi, V., De Marchi, L.: A tilt sensor node embedding a data-fusion algorithm for vibration-based SHM. Electronics 8(1), 45 (2019)
Zonzini, F., Girolami, A., De Marchi, L., Marzani, A., Brunelli, D.: Cluster-based vibration analysis of structures with graph signal processing. IEEE Trans. Ind. Electron. (2020)
Zou, Z., Bao, Y., Li, H., Spencer, B.F., Ou, J.: Embedding compressive sensing-based data loss recovery algorithm into wireless smart sensors for structural health monitoring. IEEE Sens. J. 15(2), 797–808 (2014)
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Zauli, M., Zonzini, F., Testoni, N., Marzani, A., De Marchi, L. (2021). Compressive Sensing and On-Board Data Recovery for Vibration–Based SHM. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2020. Lecture Notes in Civil Engineering, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-64594-6_33
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