High-sensitivity Accelerometer Research Articles

Development of AI-Based Multisensory System for Monitoring Quay Wall Events

Structural monitoring of quay walls, where various events occur due to unexpected high waves, vessels, and heavy equipment, is essential. However, real-time events cannot be constantly monitored by on-site personnel. To resolve the aforementioned issues, this study proposes an innovative AI-powered, cloud-based wireless sensor system that incorporates a high-sensitivity accelerometer with an ultra-low noise level of 0.003 mg, designed to monitor the low response amplitude of massive quay walls. The sensor can be activated by a scheduled trigger or a long-rangefinder. Vessel detection is performed utilizing the AI-based object detection method, Faster R-CNN, which employs ResNet as the backbone network. The detected anchor box’s position and dimensions are subsequently processed to confirm the presence of a berthing vessel. The collected data are then transmitted wirelessly to a proposed cloud server through LTE communication in real-time. The developed system was installed on a caisson-type quay wall in Korea, where acceleration, tilt, temperature, and camera image data were analyzed to assess its performance for real-time event monitoring. The results demonstrated that the safety of quay walls can be automatically managed by monitoring events during berthing and mooring with the proposed system.

Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements.

The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration-extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day.

Structural health assessment through continuous vibration-based monitoring of a stainless-steel lattened in-operation railway bridge

Damage detection in railway bridges is challenging due to changing environmental and operational conditions. Advanced monitoring techniques, such as operational modal analysis (OMA), use vibration to assess the integrity of structures during their operational life. Cov-SSI, a time-based OMA algorithm, is widely used for the automatic identification of system dynamics. Static modelling is essential to separate variability from benign effects and ensure accurate monitoring. Monitoring the health of the structure through control charts highlights damages that influence the dynamic response. This study focuses on the continuous monitoring of railway bridge networks using advanced methods such as MOVA and MOSS. In particular, this work is part of a research project dedicated to the continuous monitoring of a real bridge in operation: the Quisi Viaduct in Alicante (Spain). The viaduct is a stainless steel single-span bolted railway bridge, built at the beginning of the 20th century. The viaduct is equipped with a set of high-sensitivity accelerometers and a dense network of deformation sensors that store a compendium of data of approximately 3 years. This contribution discusses the quality of the data sets and the monitoring strategy, with a focus on the effectiveness of using statistical process control charts to assess the structural health of railway bridges.

Temperature compensation in high accuracy accelerometers using multi-sensor and machine learning methods

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications.We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting.Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers.The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy as well as to other types of measuring systems, such as magnetometers or gyroscopes.

High-Sensitivity Piezoelectric MEMS Accelerometer for Vector Hydrophones.

In response to the growing demand for high-sensitivity accelerometers in vector hydrophones, a piezoelectric MEMS accelerometer (PMA) was proposed, which has a four-cantilever beam integrated inertial mass unit structure, with the advantages of being lightweight and highly sensitive. A theoretical energy harvesting model was established for the piezoelectric cantilever beam, and the geometric dimensions and structure of the microdevice were optimized to meet the vibration pickup conditions. The sol-gel and annealing technology was employed to prepare high-quality PZT thin films on silicon substrate, and accelerometer microdevices were manufactured by using MEMS technology. Furthermore, the MEMS accelerometer was packaged for testing on a vibration measuring platform. Test results show that the PMA has a resonant frequency of 2300 Hz. In addition, there is a good linear relationship between the input acceleration and the output voltage, with V = 8.412a - 0.212. The PMA not only has high sensitivity, but also has outstanding anti-interference ability. The accelerometer structure was integrated into a vector hydrophone for testing in a calibration system. The results show that the piezoelectric vector hydrophone (PVH) has a sensitivity of -178.99 dB@1000 Hz (0 dB = 1 V/μPa) and a bandwidth of 20~1100 Hz. Meanwhile, it exhibits a good "8" shape directivity and consistency of each channel. These results demonstrate that the piezoelectric MEMS accelerometer has excellent capabilities suitable for use in vector hydrophones.

Calibration of high accuracy accelerometers for ESA missions BepiColombo and JUICE at INRIM

High sensitive triaxial accelerometers are used in several space missions to measure the non-gravitational accelerations acting on the spacecraft. Among these, the capacitive accelerometers developed for ESA missions Jupiter Icy moons Explorer (JUICE) and BepiColombo were designed to measure accelerations of the order of 3 · 10−6 m s−2 with an accuracy level of 300 ppm in the frequency range (3 · 10−5–0.1) Hz. Despite the signal to be measured is of the same order of magnitude of the seismic noise on the earth, an accurate on-ground calibration is needed. The facility set-up at INRIM to this purpose is based on a simple principle: the base of the accelerometer is dynamically tilted by an angle α so that the sensor undergoes a component of the gravitational acceleration g proportional to angle α. In practice, several issues have to be addressed by the calibration facility, such as the seismic noise limiting the signal-to-noise ratio, the generation and the accurate measurement of the tilt angle. Furthermore, the calibration was performed taking into account different on-flight conditions such as different operating temperatures and possible deformation of the accelerometers during the launch. The experimental set-up and the calibration procedure are described in the paper. The measurement results and the uncertainty budget show that a relative accuracy of 240 ppm has been achieved.

Two approaches for the passive charge management of contactless test masses

Free floating test masses (TM) of inertial reference instruments accumulate charge mainly through the triboelectric effect during separation from their housings and, if in the space environment, from cosmic radiation. These charges will degrade the accuracy of high sensitivity accelerometers and drag-free sensors. We demonstrate in ground testing two passive bipolar charge management systems using photoelectrons emitted from gold coated surfaces under illumination by ultraviolet light emitting diodes (UV-LEDs) with 255 nm, 275 nm, and 295 nm central wavelengths. The first method uses fast photoelectrons, generated by two 255 nm UV-LEDs with adjustable-intensity (through fine-tuning of their excitation currents) and illuminating the TM and its housing respectively. A second technique uses slow photoelectrons generated by one UV-LED, of either 275 nm or 295 nm, directed at the TM. Fast and slow electrons are defined as having kinetic energies after photoemission above and below , where is the maximum allowable potential required for normal operation of the TM. In its optimized configurations and following an exposure of <30 s to UV, the fast-photoelectron control system converges to zero TM potential from ±1 V with a drift of ≈1.5 mV/day. The slow-photoelectron system requires about 5 min to converge to zero TM potential from 100 mV, with a similar drift of ≈2.0 mV/day. For reference, for the LISA and LISA pathfinder sensors. Insight into the slow photoelectrons charge control method is provided by our simple Gaussian model of the work function for gold films (that approximates their quasi-triangular measured shape): 4.6 eV center, 0.2 eV standard deviation, and 4.30 eV and 4.90 eV cutoffs. The longest wavelength (lowest energy) photoemission is obtained with the 295 nm LED (11 nm FWHM) at about 285 nm (4.35 eV), where only about 3.5% of the photons have an energy above the photoelectric threshold. While the two-source UV fast photoelectron method is a modification of the LISA Pathfinder approach, the slow photoelectron method is a new passive charge management technique. These two passive techniques were developed for instruments without sensing and activation systems. For instruments with electric fields surrounding the TM and more complex geometries additional adaptations are required.

Field Measurements of Tree Dynamics with Accelerometers

A comprehensive understanding of the dynamic behavior of a tree can play a key role in the tree stability analysis. Indeed, through an engineering approach, the living tree can be modeled as a mechanical system and monitored observing its dynamic properties. In the current work, procedures of dynamic identification used in civil engineering are applied to the case study of a black locust (Robinia pseudoacacia L.). The tree was instrumented with 13 seismic, high-sensitivity accelerometers. Time histories of the tree response under ambient vibration were recorded. Three representative sections of the trunk (the collar, the diameter at breast height, and the tree fork) were equipped with three accelerometers, in order to obtain lateral and torsional vibrations. Moreover, two pairs of accelerometers were fixed on the two main branches. The results show that it is possible to identify the natural frequencies of a tree under ambient vibrations, thanks also to the support of a preliminary finite element model. Even though the optimal position is under the tree fork, the sensors fixed at the diameter at breast height allow a clear identification of the main peaks in the frequency domain.

A high sensitive, low foot print, SU-8 material-based, light intensity modulated MOMS accelerometer

A high sensitive optical MEMS accelerometer is proposed, which works by changing the effective refractive index of a microring relied on intensity modulation of light wave. The proposed optical sensing system consists of a microring, waveguide, and a proof mass. Acceleration causes lateral displacement of the proof mass. The proof mass is in the optical coupling length of the microring, and the displacement of the proof mass changes effective refractive index of the microring. Changes of the effective refractive index of the microring result in wavelength shift. The mechanical and optical parts of the proposed accelerometer are designed, and the obtained functional characteristics are compared with other accelerometers. Simulation shows functional characteristics of the proposed design are as follows: mechanical resonant frequency of 3.7 kHz, operating bandwidth of 2.6 kHz, mechanical sensitivity of 2 nm/g, optical sensitivity of 0.7%/nm, overall sensitivity of 1.4%/g, foot print of less than 150 μm * 170 μm, measurement range of ∓ 50 g, and mechanical noise density of 3.1 ( mg / Hz ) . These functional characteristics make the design very interesting for a wide range of applications, ranging from consumer electronics and automobile to inertial navigation.

Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios

This article presents an efficient methodology for the calibration of a numerical model of a Sgnss freight railway wagon based on experimental modal parameters, namely natural frequencies and mode shapes. Dynamic tests were performed for two distinct static loading configurations, tare weight and current operational overload, under demanding test conditions, particularly during an unloading operation of the train and without disturbing its tight operational schedule. These conditions impose restrictions to the tests, especially regarding the test duration, sensor positioning and system excitation. The experimental setups involve the use of several high-sensitivity accelerometers strategically distributed along with the vehicle platform and bogies in the vertical direction. The modal identification was performed with the application of the enhanced frequency-domain decomposition (EFDD) method, allowing the estimation of 10 natural frequencies and mode shapes associated with structural movements of the wagon platform, which in some cases are coupled with rigid body movements. A detailed 3D FE model of the freight wagon was developed including the platform, bogies, wheelsets, primary suspensions and wheel–rail interface. The model calibration was performed sequentially, first with the unloaded wagon model and then with the loaded wagon model, resorting to an iterative method based on a genetic algorithm. The calibration process allowed the obtainment of the optimal values of eight numerical parameters, including a double estimation of the vertical stiffness of the primary suspensions under the unloaded and loaded static configurations. The results demonstrate that the primary suspensions present an elastic/almost elastic behaviour. The comparison of experimental and numerical responses before and after calibration revealed significant improvements in the numerical models and a very good correlation between the experimental and numerical responses after calibration.

Experimental dynamic analysis of the arch road bridge

The paper presents an experimental dynamic analysis of the existing road bridge across the Labe river at Valy village in the Czech Republic. The observed structure is a bridge with 6 spans 23.1 m, 31.5 m, 84.0 m, 31.5 m and 23.1 m long. The horizontal load-bearing structure is a composite structure with two main steel girders and a lower reinforced concrete deck. The load-bearing structure is reinforced in the main span by the arch, this structural system is also called the Langer beam. The experiment was realized in three stages. The first one was performed in May 2020 before its opening, the second stage of the described experiment was realized in August 2020 and the third one was carried out in April 2021. The main purpose of the first stage was to determine in detail the natural frequencies and mode shapes of the whole bridge horizontal load-bearing structure also including the arch. The electrodynamic shaker, that was located on the bridge deck in the quarter of the main bridge span, was used for excitation of the bridge vibration. The measured characteristics of the natural vibration were compared with the calculated ones. Based on this comparison, the theoretical bridge model was verified. Basic objective of the second experiment stage was to verify new approach to dynamic response measurement – radar interferometry realized by two synchronized radars. The vibrations of the bridge caused by the standard road traffic and also by pedestrians were observed concurrently by both radar interferometry and classical approach realized by high sensitive piezoelectric accelerometers. The experiment was focused on the main span of the bridge only and the levels of forced vibration were observed primarily. However, the fundamental natural frequencies were also evaluated. The third stage was carried out by classical approach only. Again, the bridge vibration caused by the usual road traffic and pedestrians were measured in the main bridge span only because this section of the bridge was the most dynamically sensitive. Again, the levels of forced vibration were observed and the fundamental natural frequencies were determined. The evaluated natural frequencies from all three experiment stages were consequently compared.

Dynamic Identification Tests of 20th Century Historic Masonry Buildings in Japan

This paper discussed the application of health monitoring systems to 20th-century historic buildings. Natural disasters are major threats to monuments. They are often seismically vulnerable and require interventions. However, taking into account their historic and cultural values, it is appropriate to observe long-term behaviour before making a decision on intervention schemes. To this aim, health monitoring is considered an effective approach. In recent years, MEMS (micro-electromechanical systems) accelerometers have been attracting attention for their convenience and efficacy. Nonetheless, the reliability of MEMS accelerometers still needs to be examined for the monitoring of monuments as sufficient research contributions have not been made. This paper presented two case studies that were monitored by means of MEMS accelerometers. They were masonry structures positioned in seismic-prone regions in Japan. A number of earthquakes were detected by the accelerometers during one year of monitoring. To examine the accuracy of the adopted MEMS accelerometers, dynamic identification tests were conducted using high-sensitivity strain-gauge accelerometers and servo velocity meters. Based on responses obtained from the tests, numerical simulation was performed. Nonlinear static analysis was performed. The numerical simulation permitted the comparison of reliability among sensors and test types. This paper provided suggestions for the dynamic identification tests of heritage structures.

(Invited) A Highly Sensitive MEMS Accelerometer Module for Measuring Micro Muscular Sounds

This paper introduces a high-sensitivity accelerometer module to measure micro muscular sounds. The feature of our microelectromechanical systems (MEMS) accelerometer is the use of high-density gold proof mass structure fabricated by the multi-layer metal technology for reducing the Brownian noise. The noise floor of the proposed module is 10-dB better than that of a commercial one. The measurement results showed that our module can capture small muscular sounds which are buried in the noise of the commercial accelerometer.

(Invited) A Highly Sensitive MEMS Accelerometer Module for Measuring Micro Muscular Sounds

This paper presents a high-sensitivity accelerometer module to measure micro muscular sounds. The feature of our microelectromechanical systems (MEMS) accelerometer is the use of high-density gold proof-mass structure fabricated by the multi-layer metal technology for reducing the Brownian noise. The noise floor of the proposed module is 10-dB better than that of a commercial one. The measurement results showed that our module can capture small muscular sounds which are buried in the noise of the commercial accelerometer.

Pull-In Dynamics of Two MEMS Parallel-Plate Structures for Acceleration Measurement

This paper investigates the pull-in dynamics of single-sided and double-sided parallel-plate (SSPP and DSPP) structures employed in MEMS devices for acceleration measurement. A lumped electromechanical model considering a variable damping coefficient and external acceleration is developed for studying their pull-in dynamical behavior. The critical pull-in displacement and pull-in voltage for both parallel-plate structures operated with external acceleration are derived based on the static and dynamical analysis of the proposed model, which are in good agreement with the experimental results. The derived closed-form solutions are shown to be capable of predicting the pull-in behavior of both parallel-plate structures in an overdamped system. Two MEMS parallel-plate structures exhibit distinct pull-in characteristics due to their distinct configurations. The commonly used SSPP structure for MEMS devices is shown to have a relatively linear response to the external acceleration in terms of the critical pull-in displacement and pull-in voltage. However, the DSPP structure exhibits much larger yet nonlinear sensitivity of both the critical pull-in displacement and the critical pull-in voltage to a small range of external acceleration, which has the potential of developing high-sensitivity accelerometers whenever the sensitivity nonlinearity is compensated. Moreover, the effects of pressure are characterized for both DSPP and SSPP devices, which affects the difference between the critical static and dynamic pull-in voltages. The SSPP structure is shown to have the critical pull-in voltage proportional to the logarithmic magnitude of the pressure, while that of the DSPP structure do not vary linearly with respect to the logarithmic magnitude of the pressure.

Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach

Traffic signal mast arm structures must be regularly inspected for cracking, bolt loosening, and other signs of deterioration. Due to large inventories, physical inspections and/or dedicated monitoring systems can be prohibitively time-consuming and expensive to implement at a large scale. However, the growing use of vision-based methods for structural monitoring applications introduces the possibility of leveraging video footage from existing traffic cameras for this purpose. The extraction of dynamic properties (i.e., natural frequencies and damping) from this footage could be employed in detecting possible signs of deterioration. This study presents a vision-based monitoring method which uses a single traffic camera to identify the modal properties of the supporting traffic signal mast arm. This was achieved via operational modal analysis on pixel displacements obtained from a traffic camera mounted on a traffic signal mast arm in Norfolk, VA, monitored during July, 2019. First, sub-pixel displacements were extracted frame-by-frame using weighted centroid tracking of pavement markings. Then, covariance-driven stochastic subspace identification (SSI-Cov) was employed to extract the mast arm fundamental frequencies, damping ratios, and mode shapes. For validation of the vision-based results, SSI-Cov was also applied to acceleration data recorded by two high-sensitivity accelerometers mounted on the structure. In total, the processing was carried out on four different videos and ten acceleration datasets. The vision-based method was able to reliably identify the fundamental frequencies of the structure (Δf < 0.005 Hz mean difference). The associated damping ratios were consistently overestimated but still close in structural terms (Δζ < 0.7% mean difference).

Vibration-based structural health monitoring of a RC-masonry tower equipped with non-conventional TMD

This article presents the development and the results of three years of implementation of an automated vibration-based structural health monitoring system for modal tracking the dynamic characteristics of a concrete-masonry Civic Tower in Rieti (Italy) equipped with a passive vibration control system, a Non-Conventional Tuned Mass Damper. The system is based on the data recorded by a small number of high-sensitivity accelerometers set on remote automated modal parameters tracking. The analysis of monitoring data highlights the main characteristics of the response of the tower to operational vibrations and low-return period earthquakes. Despite the low levels of vibration in operational conditions, the system can track the evolution of the structural frequencies along time and successfully capture their dependence from temperature, both daily and seasonally. Moreover, the robustness of the modal identification procedure allowed the detection of anomalous variation from the validated reference dynamics of the structure.

IoT-Based Unobtrusive Sensing for Sleep Quality Monitoring and Assessment

Aging population is a worldwide trend, which has created a societal crisis as many countries face the challenges of supporting an aging population with increasing costs of healthcare and decreasing numbers of caregivers. Sleep related disorders are common diseases, especially among the elderly. In this paper, we propose a simple and affordable unobtrusive sensing environment including a high-sensitive accelerometer on a bed and passive infrared (PIR) motion sensors in every room, following the generic framework of Internet of Health Things (IoHT) for monitoring the elderly's sleep-wake conditions, to assess their sleep quality. The environment is nonintrusive, comfortable and can be used for long-term sleep monitoring, detecting early symptoms of sleep related disorders, and responding to caregivers. We implement and pilot test the environment under different daily living situations related to sleep quality. We develop a feature extraction algorithm and applied five popular data analytics models to assess their relative performance. Our study shows that all classifiers except Naïve Bayes can effectively detect sleep quality with the Area under ROC curve (AUC) performance higher than 90%. Among which multilayer feed-forward neural network achieved the best results, in which the detecting sensitivity is up to 96.61%, specificity is 91.81% and AUC performance is 94.21%.

Design and Modeling of a High Sensitivity Fiber Bragg Grating-Based Accelerometer

Use of a detailed theoretical model has allowed the optimization of the design of a high sensitivity accelerometer, based on a fiber Bragg grating (FBG) and an accelerometer based on this design has been demonstrated experimentally. With a universal model based on double-point encapsulation established, the performance of the device in terms of its optimal sensitivity and frequency distribution has been analyzed, with an optimization ‘figure of merit’ using the product of the sensitivity and the resonant frequency being presented. The experimental results obtained indicate that the FBG-based accelerometer thus developed shows a broad, flat frequency band, a corresponding flat range sensitivity of ~152.0pm/G, a resonant frequency of 441.0Hz, and a cross-axis sensitivity of less than 3.6% of the main-axis sensitivity. An accelerometer of this type and with this performance thus has the potential for the important field of low frequency oil-gas seismic exploration.

A liquid-state high sensitive accelerometer based on a micro-scale liquid marble

In this paper, the dynamic behavior of a high sensitive liquid marble based accelerometer in the presence of the external acceleration field is investigated. The presented scheme for this study consists of a liquid marble placed on a curved surface bounded with another fixed surface above it. In order facilitate the marble displacement and therefore increase the accelerometer sensitivity, the marble is manipulated electrostatically by applying a DC voltage to the fixed top and bottom curved surfaces. The produced electrostatic force deforms the marble by opposing its weight, therefore causes the marble to get spherical form approximately. In the following, the nonlinear equation governing the motion of the liquid marble in the existence of the external acceleration is derived. To study the marble stationary behavior, the equation has been solved by utilizing the weighted residual method. It has been shown that by decreasing the contact zone area of the marble, the minimum external acceleration required to start the rolling motion of the liquid marble decreases. Investigating the frequency response of the accelerometer has also shown that for the external acceleration value lower than 0.04 g, the accelerometer shows linear stationary behavior.