Wireless MEMS-Based Accelerometer Sensor Boards for Structural Vibration Monitoring: A Review

Sabato, Alessandro; Niezrecki, Christopher; Fortino, Giancarlo

doi:10.1109/jsen.2016.2630008

Cited by 244 publications

(97 citation statements)

References 50 publications

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“…Current state-of-the-art low g MEMS accelerometers are capable of sensing sub mg accelerations with noise densities of around 0.1 mg/Hz 1/2 or less. [3][4][5] A typical MEMS accelerometer proof-mass is roughly one microgram, meaning the devices are capable of resolving forces below 1 pN. Such force sensitivity is comparable to the performance of an atomic force microscope (AFM) but is realized on a single mm-scale chip and costs just tens of dollars per device.…”

Section: Introductionmentioning

confidence: 99%

Building a Casimir metrology platform with a commercial MEMS sensor

et al. 2019

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The Casimir Effect is a physical manifestation of quantum fluctuations of the electromagnetic vacuum. When two metal plates are placed close together, typically much less than a micron, the long wavelength modes between them are frozen out, giving rise to a net attractive force between the plates, scaling as d −4 (or d −3 for a spherical-planar geometry) even when they are not electrically charged. In this paper, we observe the Casimir Effect in ambient conditions using a modified capacitive micro-electromechanical system (MEMS) sensor. Using a feedback-assisted pick-and-place assembly process, we are able to attach various microstructures onto the post-release MEMS, converting it from an inertial force sensor to a direct force measurement platform with pN (piconewton) resolution. With this system we are able to directly measure the Casimir force between a silver-coated microsphere and gold-coated silicon plate. This device is a step towards leveraging the Casimir Effect for cheap, sensitive, room temperature quantum metrology.

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Section: Introductionmentioning

confidence: 99%

Building a Casimir metrology platform with a commercial MEMS sensor

et al. 2019

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“…Such movements can be measured with an inertial measurement unit (IMU), particularly if the unit is installed near the mounding blade of the excavator. These sensors have developed into low-cost devices due to their extensive use in numerous applications, including robotics (Botero Valencia et al 2017;Menna et al 2017), vibration detection (Singleton et al 2017;Sabato et al 2017), and human activity recognition (Pavey et al 2017;Zdravevski et al 2017). As the mounding blade of the excavator usually penetrates the soil surface at the same angle between the mounds, the subsequent vibrations and shocks to the excavator boom may correlate with the stone content of the soil.…”

Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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Highlights• An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work. • Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements. • The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells. AbstractThe stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20-30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil's stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

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“…The core criterion of digital image correlation is to search for predefined patterns in sequential images. Zero mean normalized cross-correlation and peak finding have demonstrated high resolution for remote inspection [22,23]. The quantitative comparisons in the temporal domain and frequency domain have shown the experimental accuracy of the self-developed vibration monitoring system (VMS) system.…”

Section: Introductionmentioning

confidence: 98%

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

Chang

Huang

2019

Sensors

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This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation.

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Wireless MEMS-Based Accelerometer Sensor Boards for Structural Vibration Monitoring: A Review

Cited by 244 publications

References 50 publications

Building a Casimir metrology platform with a commercial MEMS sensor

Building a Casimir metrology platform with a commercial MEMS sensor

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

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