HW-Oriented Compressed Sensing for Operational Modal Analysis: The Impact of Noise in MEMS Accelerometer Networks

Nowadays, there is an increasing demand for resilient and long-term monitoring solutions, capable to enhance the safety of aging structures against man-made and built-in hazards. Nonetheless, the widespread deployment of full-scale and dense sensor networks might be incompatible with the available energy budget. Besides, the massive amount of data which is acquired might cause network congestion. To address these issues, the Compressed Sensing (CS) technique represents a solution that is cost-effective and specifically suited for the vibration diagnostics field. This work investigates the feasibility of a model-based CS technique, exploiting the so-called rakeness (Rak-CS) approach, which is robust against noise uncertainty in the context of pure ambient vibrations. Experimental results proved that the accuracy of the reconstructed structural parameters is up to 95 % (i.e. modal shape correlation equal to 0.95) with a compression ratio equal to 10.