Evaluation of low-cost MEMS accelerometers for SHM: frequency and damping identification of civil structures

Ribeiro, Renan Rocha; Lameiras, Rodrigo Melo

doi:10.1590/1679-78255308

Cited by 47 publications

(27 citation statements)

References 31 publications

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“…However, for civil building structures, these techniques have only been applied as early warning systems (EWS) in areas with severe seismic activity [ 13 ], and less intensely as an NDT on historic timber structures [ 14 ]. There exist a variety of types of accelerometers, being the ones based on piezoelectric crystals and those embedded in Micro Electro-Mechanical Systems (MEMS) the most spread ones [ 15 , 16 , 17 ]. In general terms, the piezoelectric sensors produce higher quality measurements than the MEMS sensors, but at a significant higher cost.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis

Magdaleno

Villacorta

Val

et al. 2021

Sensors

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One of the most popular options in the Structural Health Monitoring field is the tracking of the modal parameters, which are estimated through the frequency response functions of the structure, usually in the form of accelerances, which are computed as the ratio between the measured accelerations and the applied forces. This requires the use of devices capable of synchronously recording accelerations at several points of the structure at high sampling rates and the subsequent computational analysis using the recorded data. To this end, this work presents and validates a new scalable acquisition system based on multiple myRIO devices and digital MEMS (Micro-Electro-Mechanical System) accelerometers, intended for modal analysis of large structures. A simple form of this system was presented by the authors in a previous work, showing that a single board with some accelerometers connected to it got to obtain high quality measurements in both time and frequency domains. Now, a larger system composed by several slave boards connected and synchronized to a master one is presented. Delays lower than 100 ns are found between the synchronised channels of the proposed system. For validation purposes, a case study is presented where the devices are deployed on a timber platform to estimate its modal properties, which are compared with the ones provided by a commercial system, based on analog accelerometers, to show that similar results are obtained at a significantly lower cost.

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Section: Introductionmentioning

confidence: 99%

Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis

Magdaleno

Villacorta

Val

et al. 2021

Sensors

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“…After the innovative development of low-cost MEMS accelerometers with a reasonable behavior in terms of resolution, sensitivity and noise level [ 113 ], it would be interesting to increase the number of studies focusing in the engineering of such sensors in real situations, involving the damping ratio and evaluation/identification of other modal parameters, such as mode shapes.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Magnetoelectrics: Three Centuries of Research Heading Towards the 4.0 Industrial Revolution

et al. 2020

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Magnetoelectric (ME) materials composed of magnetostrictive and piezoelectric phases have been the subject of decades of research due to their versatility and unique capability to couple the magnetic and electric properties of the matter. While these materials are often studied from a fundamental point of view, the 4.0 revolution (automation of traditional manufacturing and industrial practices, using modern smart technology) and the Internet of Things (IoT) context allows the perfect conditions for this type of materials being effectively/finally implemented in a variety of advanced applications. This review starts in the era of Rontgen and Curie and ends up in the present day, highlighting challenges/directions for the time to come. The main materials, configurations, ME coefficients, and processing techniques are reported.

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“…These outcomes obtained on high-noise datasets show the potentialities of the proposed approach in view of facing real engineering applications. Indeed, noise effect is a principal concern especially when pervasive and low-cost microelectromechanical systems (MEMS) sensor networks are employed [39], so that the possibility to handle it through FCNs may enhance the application of MEMS networks. Moreover, thanks to our procedure, we have been able to avoid the data pre-processing required by any ML approach based on problem specific features.…”

Section: Damage Detection and Localization In Case 1-sinusoidal Load mentioning

confidence: 99%

Fully convolutional networks for structural health monitoring through multivariate time series classification

Rosafalco

Manzoni

Mariani

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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We propose a novel approach to structural health monitoring (SHM), aiming at the automatic identification of damage-sensitive features from data acquired through pervasive sensor systems. Damage detection and localization are formulated as classification problems, and tackled through fully convolutional networks (FCNs). A supervised training of the proposed network architecture is performed on data extracted from numerical simulations of a physics-based model (playing the role of digital twin of the structure to be monitored) accounting for different damage scenarios. By relying on this simplified model of the structure, several load conditions are considered during the training phase of the FCN, whose architecture has been designed to deal with time series of different length. The training of the neural network is done before the monitoring system starts operating, thus enabling a real time damage classification. The numerical performances of the proposed strategy are assessed on a numerical benchmark case consisting of an eight-story shear building subjected to two load types, one of which modeling random vibrations due to low-energy seismicity. Measurement noise has been added to the responses of the structure to mimic the outputs of a real monitoring system. Extremely good classification capacities are shown: among the nine possible alternatives (represented by the healthy state and by a damage at any floor), damage is correctly classified in up to $$95 \%$$ 95 % of cases, thus showing the strong potential of the proposed approach in view of the application to real-life cases.

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Evaluation of low-cost MEMS accelerometers for SHM: frequency and damping identification of civil structures

Cited by 47 publications

References 31 publications

Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis

Measurement of Acceleration Response Functions with Scalable Low-Cost Devices. An Application to the Experimental Modal Analysis

Magnetoelectrics: Three Centuries of Research Heading Towards the 4.0 Industrial Revolution

Fully convolutional networks for structural health monitoring through multivariate time series classification

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