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

Testoni, Nicola; Aguzzi, Cristiano; Arditi, Valentina; Zonzini, Federica; Marchi, Luca De; Marzani, Alessandro; Cinotti, Tullio Salmon

doi:10.1155/2018/2107679

Cited by 26 publications

(15 citation statements)

References 34 publications

(36 reference statements)

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“…Micro Electro-Mechanical Systems (MEMS) devices are characterized by high sensitivity, low-power consumption and very high integration levels, which made this sensing technology a cost-effective yet reliable and extremely advantageous alternative to the piezolectric counterpart for the design of accelerometer sensors [ 54 ]. Indeed, the advent of MEMS sensors made the widespread development of low-cost, dense and miniaturized sensor networks a real opportunity in the context of SHM [ 55 ].…”

Section: Experimental Validationmentioning

confidence: 99%

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Zonzini

Carbone

Romano

et al. 2022

Sensors

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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.

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Section: Experimental Validationmentioning

confidence: 99%

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Zonzini

Carbone

Romano

et al. 2022

Sensors

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“…The original solution is the adoption of a multi-channel radio communication on different frequency channels to improve the data transmission capability and the reliability of the WSN. Embedded computational resources in sensors nodes for vibration monitoring has been designed and tested on a laboratory mock up by Testoni et al [ 148 ]; this work shows a node design with volume/weight constraints and low power consumption for implementing a wired sensor network based on PCL. A dramatic gain in volume factor for integrated sensor node is achievable by integrated electronic design.…”

Section: Hardware Developments Of Wired and Wireless Sensor Network (Wsns) For Shm And Validation Testsmentioning

confidence: 99%

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Capineri

Bulletti

2021

Sensors

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This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

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“…A low-power and low-cost Smart Sensor Network (SSN) [32] was used to experimentally validate the proposed approach. Each peripheral sensor node is equipped with a microcontroller unit (MCU) and a tri-axial Micro-electromechanical System (MEMS) inertial measurement unit.…”

Section: Shm Architecturementioning

confidence: 99%

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

Zonzini

Zauli

Mangia

et al. 2021

IEEE Trans. Ind. Inf.

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The main challenge in the implementation of longlasting vibration monitoring systems is to tackle the constantly evolving 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 rakeness-based compressed sensing (Rak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data. In particular, a novel model-assisted variant (MRak-CS) is proposed, which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the Wavelet Packet Transform operator is conceived, which aims at maximizing the signal sparsity while allowing for a precise timefrequency localization.The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the rakeness-based 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.

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

Cited by 26 publications

References 34 publications

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review

Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

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