Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations

A Combination of Chirp Spread Spectrum and Frequency Hopping for Guided Waves-based Digital Data Communication with Frequency Steerable Acoustic Transducers

To facilitate Guided Waves (GWs) communication in terms of hardware simplification and cost reductions, shaped transducers with inherent directional properties can be used. A promising example of such devices is provided by Frequency Steerable Acoustic Transducers (FSATs), where the propagation direction of waves is controlled by the frequency content of the transmitted/acquired signals, thanks to the spatial filtering effect. These peculiar characteristics make the FSAT devices particularly suited for implementation of frequency-based modulation protocols, in which the signal content assigned to each user is uniquely encoded by a corresponding carrier tone. In this work, the special directivity of FSATs is paired with a novel encoding strategy, which is based on a combination of Chirp Spread Spectrum (CSS) and Frequency Hopping (FH) multiplexing, similar to the LoRaWan solution adopted in radio-frequency environments. The devised strategy is aimed at suppressing the inherent destructive interference due to GWs dispersion and multi-path fading

Enabling Spatial Multiplexing in Guided Waves-based Communication: the case of Quadrature Amplitude Modulation realized via Discrete Frequency Steerable Acoustic Transducers

Guided Waves (GWs) communication using conventional transducers, e.g., PZT, encounters quite a few problems, such as complex hardware systems and waves multipath interference. To overcome such drawbacks, Frequency Steerable Acoustic Transducers (FSATs) which benefit from inherent directional capabilities can be fruitfully adopted to implement a spatial multiplexing strategy. The FSATs work on the frequency-dependent spatial filtering effect to generate/receive waves, resulting in a direct relationship between the direction of propagation and the frequency content of the transmitted/received signals. Thanks to this unique frequency-steering capability, FSATs are best suited to implement frequency-driven modulation protocols, such as the ones typically exploited for GWs-based data communication. Among these, the Quadrature Amplitude Modulation (QAM) scheme is advantageous in terms of noise immunity. Thus, the objective of this work is to combine QAM with the built-in spatial multiplexing capabilities of FSATs to realize, in hardware, frequency directivity, like the solutions that are currently being investigated in 5G communications

Vibration Monitoring in the Compressed Domain with Energy-Efficient Sensor Networks

Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively

Cluster-based Vibration Analysis of Structures with GSP

This article describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of microelectromechanical accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme, which consists of unconventional and nonoverlapped sensing configurations. Signal processing techniques for inter- and intracluster data assembly are introduced to allow for a fullscale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, reveals high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes are correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach that favors the implementation of autonomous smart monitoring systems

A Tiny Convolutional Neural Network driven by System Identification for Vibration Anomaly Detection at the Extreme Edge

Vibration data analysis is the driving tool for the Structural Health Monitoring (SHM) of structures in the dynamic regime, i.e., structures showing important oscillatory behaviours, which largely dominate the transportation back-bone: from terrestrial/aerial vehicles (e.g., trains, aircraft, etc.) to the supporting infrastructures (e.g., bridges, viaducts, etc.). Outstanding opportunities have recently been disclosed in the field of Intelligent Transportation Systems (ITS) by the advent of sensor-near processing functionalities, eventually empowered by Artificial Intelligence (AI). The latter allow for the extraction of damage-sensitive features at the extreme edge, without the need of transmitting long time series over the monitoring network. In this work, we explore for the first time a novel anomaly detection workflow for on-sensor vibration diagnostics, which combines the unique advantages of embedded System Identification (eSysId) as a data compression strategy with the computational/energy advantages of Tiny Machine Learning (TinyML). Experimental results conducted on a representative SHM dataset demonstrate that the proposed pipeline can achieve high classification scores (above 90%) for the health assessment of the well-known Z24 bridge. In particular, the minimal inference time (less than 44 ms) and power consumption performed while running on three different general-purpose microprocessors make it a promising solution for the development of the next generation of SHM-oriented ITS

A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM

This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges

An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases – from their design to their deployment, to their management –, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks

Caratterizzazione sperimentale di sistemi di localizzazione a banda ultra-larga

Il processo di localizzazione risponde all'esigenza dell'uomo di avere una percezione sempre più dettagliata e precisa del contesto in cui si trova, con l'intento di migliorare e semplificare l'interazione con oggetti e cose ivi presenti. L'idea attuale è quella di progettare sistemi di posizionamento con particolare riguardo agli ambienti indoor, caratterizzati da proprietà e densità di elementi che limitano fortemente le prestazioni dei consolidati sistemi di tracking, particolarmente efficienti in spazi aperti. Consapevole di questa necessità, il seguente elaborato analizza le prestazioni di un sistema di localizzazione sviluppato dall'Università di Bologna funzionante in tecnologia Ultra-Wide Bandwidth (UWB) e installato nei laboratori DEI dell'Alma Mater Studiorum con sede a Cesena. L'obiettivo è quello di caratterizzare l'accuratezza di localizzazione del sistema, suggerendo nuovi approcci operativi e de�finendo il ruolo dei principali parametri che giocano nel meccanismo di stima della posizione, sia in riferimento a scenari marcatamente statici sia in contesti in cui si ha una interazione dinamica degli oggetti con lo spazio circostante. Una caratteristica della tecnologia UWB è, infatti, quella di limitare l'errore di posizionamento nel caso di localizzazione indoor, grazie alle caratteristiche fisiche ed elettriche dei segnali, aprendo nuovi scenari applicativi favorevoli in termini economici, energetici e di minore complessità dei dispositivi impiegati

Tecniche di signal processing per l'analisi modale in applicazioni SHM

L'obiettivo della disciplina nota con l'acronimo SHM (Structural Health Monitoring) è quello di incrementare la sicurezza di infrastrutture critiche, monitorandone in tempo reale l'integrità attraverso l'implementazione di sistemi sensoriali embedded. In questo contesto il presente lavoro di tesi riguarda, in particolare, lo sviluppo di due reti di sensori accelerometrici microelettromeccanici per l'analisi della dinamica di strutture attraverso l'estrazione di parametri modali, quali frequenze naturali di vibrazione e forme modali. Tecniche di signal processing sono state sviluppate per calcolare la densità spettrale di potenza (PSD) dei dati di accelerazione acquisiti, sulla base di approcci parametrici e non parametrici. Algoritmi nel dominio del tempo (TDD) e della frequenza (FDD), insieme all'identificazione a sorgenti indipendenti del secondo ordine (SOBI), sono stati implementati per la ricostruzione delle curve modali. L'affidabilità delle architetture studiate è stata valutata prima di tutto in condizioni operative nominali, quindi simulando sperimentalmente situazioni di anomalia di varia entità, così come impostando differenti schemi di acquisizione hardware e software. Gli esiti dell'elaborazione hanno mostrato in entrambi i casi un'alta e soddisfacente corrispondenza tra il modello analitico di riferimento e la risposta degli algoritmi implementati. Versatilità, facilità di riconfigurazione, scalabilità e compatibilità con installazioni a lungo termine sono alcuni fra i più importanti vantaggi delle circuiterie presentate. Il peso leggero e i costi ridotti, unitamente alla robustezza delle tecniche di elaborazione dei dati registrati, potenziano inoltre le capacità del sistema di fornire informazioni in tempo reale