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A Small Footprint, Low Power, and Light Weight Sensor Node and Dedicated Processing for Modal Analysis

  • Federica ZonziniEmail author
  • Luca De Marchi
  • Nicola Testoni
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 539)

Abstract

Structural Health Monitoring functionalities are aimed at constantly assessing the health of a building in order to prevent dramatic consequence of a damage. This work describes a well-defined wireless sensor network system installed over a steel beam capable to perform modal parameters estimation, such as natural vibration frequencies and modal shapes. Signal Processing Techniques were aimed at computing Power Spectral Density of the acceleration signals acquired, dealing with parametric and non parametric approaches. Algorithms in frequency domain, together with the Second Order Blind Identification method were implemented for modal shapes reconstruction. Beside a satisfactory agreement between the theoretical model and the output response of the algorithms implemented, versatility, easiness of reconfiguration, scalability and compatibility with long term installation are among the most powerful advantages of the architecture proposed. Light weight, low power consumption also enhance the capabilities of the system to provide real-time information in a relatively cheap way.

Keywords

Modal analysis Structural health monitoring Low power 

References

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    Hoa, L.T., Tamura, Y., Yoshida, A., Anh, N.D.: Frequency domain versus time domain modal identifications for ambient excited structures. In: International Conference on Engineering Mechanics and Automation (ICEMA 2010), pp. 1–2 (2010)Google Scholar
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    Stoica, P., Moses, R.L., et al.: Spectral Analysis of Signals, vol. 1. Pearson Prentice Hall, Upper Saddle River (2005)Google Scholar
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    Poncelet, F., Kerschen, G., Golinval, J.-C.: Experimental modal analysis using blind source separation techniques. In: International Conference on Noise and Vibration Engineering, Leuven (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Federica Zonzini
    • 1
    Email author
  • Luca De Marchi
    • 1
  • Nicola Testoni
    • 1
  1. 1.DEIUniversity of BolognaBolognaItaly

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