The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle the complexity of modern 'mesoscale' structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, on-board storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the model-assisted rakeness-based compressed sensing (MRak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the Wavelet Packet Transform is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the proposed compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to 7 and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.

Model-assisted Compressed Sensing for Vibration-based Structural Health Monitoring

Zonzini, Federica
Primo
;
Zauli, Matteo
Secondo
;
Mangia, Mauro;Testoni, Nicola;De Marchi, Luca
2021

Abstract

The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle the complexity of modern 'mesoscale' structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, on-board storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the model-assisted rakeness-based compressed sensing (MRak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the Wavelet Packet Transform is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the proposed compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to 7 and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.
Zonzini, Federica; Zauli, Matteo; Mangia, Mauro; Testoni, Nicola; De Marchi, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/788080
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