Directional Multifrequency Guided Waves Communications Using Discrete Frequency-Steerable Acoustic Transducers.

A novel directional transducer based on guided waves (GWs) is introduced in this article, designed for use in structural health monitoring (SHM) and acoustic data communication applications, i.e., systems in which the elastic medium serves as a transmission channel and information is conveyed through the medium via elastic waves. Such systems can overcome difficulties associated with traditional communication methods like wire-based or radio frequency (RF), which can be complex and have limitations in harsh environments or hard-to-reach places. However, the development of these techniques is hampered by GW dispersive and multimodal propagation and by multipath interference. The shortcomings can be effectively addressed by employing frequency steerable acoustic transducers (FSATs), which leverage their inherent directional capabilities. This can be achieved through the exploitation of a frequency-dependent spatial filtering effect, yielding a direct correlation between the frequency content of the transmitted or received signals and the direction of propagation. The proposed transducer is designed to actuate or sense the A0 Lamb wave propagating in three orientations using varying frequencies and has three channels with distinct frequencies for each direction, ranging from 50 to 450 kHz. The transducer performance was verified through finite element (FE) simulations, accompanied by experimental testing using a scanning laser Doppler vibrometer (SLDV). The unique frequency-steering capability of FSATs is combined with the ON-OFF keying (OOK) modulation scheme to achieve frequency directivity in hardware, similar to ongoing research in 5G communications. The multiple-in-multiple-out (MIMO) capabilities of the transducer were finally tested over a thin aluminum plate, showing excellent agreement with the FE simulation results.

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring.

Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data analytics and structural assessment task. Besides, one of the open problems in the field relates to the communication layer of the sensor networks since the continuous collection of long time series from multiple sensing units rapidly consumes the available memory resources, and requires complicated protocol to avoid network congestion. In this scenario, the present work presents a comprehensive framework for vibration-based diagnostics, in which data compression techniques are firstly introduced as a means to shrink the dimension of the data to be managed through the system. Then, neural network models solving binary classification problems were implemented for the sake of damage detection, also encompassing the influence of environmental factors in the evaluation of the structural status. Moreover, the potential degradation induced by the usage of low cost sensors on the adopted framework was evaluated: Additional analyses were performed in which experimental data were corrupted with the noise characterizing MEMS sensors. The proposed solutions were tested with experimental data from the Z24 bridge use case, proving that the amalgam of data compression, optimized (i.e., low complexity) machine learning architectures and environmental information allows to attain high classification scores, i.e., accuracy and precision greater than 96% and 95%, respectively.

Deep Learning Approaches for Robust Time of Arrival Estimation in Acoustic Emission Monitoring.

In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of standard statistical strategies which cannot handle, with enough robustness, very noisy scenarios and, thus, cannot be sufficiently reliable when the signal statistics are perturbed by local drifts or outliers. This concept was validated with two experiments: the pure ToA identification capability was firstly assessed on synthetic signals for which a ground truth is available, showing a 10× gain in accuracy when compared to the classical Akaike information criterion (AIC). Then, the same models were tested via experimental data acquired in the framework of a localization problem to identify targets with known coordinates on a square aluminum plate, demonstrating an overreaching precision under significant noise levels.