This paper deals with a preliminary assessment of the performance of four Compressive Sampling (CS) algorithms used for Acoustic Emission (AE) signals delivered by a distributed Structural Health Monitoring (SHM) system. In particular, three random CS-based methods (i.e., Random Demodulation, Gaussian, and Bernoulli), already available in the literature, were evaluated and compared to a deterministic CS-based approach, called Deterministic Binary Block Diagonal (DBBD). The obtained experimental results show that the CS-based method relying on the DBBD outperforms the efficiency of the random CS-based approaches in terms of signal reconstruction quality. In particular, the figure of merit Recovery Error (RE) has been calculated and it is shown that REs are below 20% for compression ratios up to 6 in the case of DBBD CS method.
A CS-based acquisition method of acoustic emission signals from distributed SHM systems
De Vito L.;Lamonaca F.;Picariello F.;Tudosa I.
2022-01-01
Abstract
This paper deals with a preliminary assessment of the performance of four Compressive Sampling (CS) algorithms used for Acoustic Emission (AE) signals delivered by a distributed Structural Health Monitoring (SHM) system. In particular, three random CS-based methods (i.e., Random Demodulation, Gaussian, and Bernoulli), already available in the literature, were evaluated and compared to a deterministic CS-based approach, called Deterministic Binary Block Diagonal (DBBD). The obtained experimental results show that the CS-based method relying on the DBBD outperforms the efficiency of the random CS-based approaches in terms of signal reconstruction quality. In particular, the figure of merit Recovery Error (RE) has been calculated and it is shown that REs are below 20% for compression ratios up to 6 in the case of DBBD CS method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
