Multiple Sensor Locations Research Articles

A full computational framework for the temperature analysis of a flax-reinforced composite footbridge with field monitoring validation

This research presents a computational framework for predicting temperatures in flax-reinforced composite sandwich footbridges. The case study is a 15m-span footbridge situated in Almere, the Netherlands, instrumented with 82 fiber Bragg grating (FBG) sensors and 8 thermocouples within an IoT-based structural health monitoring (SHM) system, enabling real-time evaluation. The main focus of this article is to enhance the understanding of structural temperature distributions in FRP sandwich cross-sections for bridge engineering applications. Furthermore, providing a comprehensive and accurate framework for temperature normalization of FBG strain measurements. The methodology involves environmental data, point-by-point solar radiation analysis with sun sheltering, and thermal properties characterization, culminating in a numerical analysis using Abaqus. A k-d tree binary search is performed previous to the numerical analysis to solve compatibility between Abaqus and Rhino's meshing algorithms. The non-uniform heat flux is used as input for the numerical analysis and handled through UEXTERNALDB and DFLUX subroutines. The use of a sub-modeling technique, allows the isolation, in correspondence of the sensor locations, of one critical section of the bridge for detailed analysis. Verification against in-situ measurements demonstrates the framework's efficacy. It was concluded that the framework restitutes errors in the range of ±1 ÷ 3 °C across varying weather conditions, during different seasons, and consistently for multiple sensor locations. Finally, the results are used to compensate strain measurements of nearby selected FBG sensors from the temperature counterparts.

A novel multi-level data fusion and anomaly detection approach for infrastructure damage identification and localisation

In this paper, a new damage identification and localisation framework utilising multi-level data fusion and anomaly detection techniques is introduced. The performance of the framework is presented via case studies of a simply supported bridge and a continuous bridge, whose data were numerically generated through a surrogate model. Accelerations, deflections and bending moments obtained at multiple sensor locations when the bridge was subjected to a moving vehicle were used as the input. A damage-sensitive feature was established via coupling principal component analysis and Mahalanobis distance, allowing for initial data dimensionality reduction and information integration. Anomaly detection using a deep convolutional autoencoder was performed to identify the presence of single and multiple damages on the bridge. It is demonstrated that the proposed approach is independent of the mass and speed of moving vehicles, and is applicable to different types of bridges (i.e. simply supported bridge and continuous bridge) with only minor modifications to the data augmentation/preparation processes. The accuracy of damage identification for the simply supported bridge is shown to be consistently greater than 93%, even for extremely small damage severity (e.g. 1% reduction in local stiffness), and multiple damages. While the damage identification accuracy for the continuous bridge is greater than 94.9% for the majority of the tested cases. The proposed damage localisation approach can accurately determine damage location(s) for both single and multiple damage cases. It is shown how the proposed framework can be adopted for early damage detection to enable reliable decision-making and maximise structural safety.

Capacitive eye tracker made of fractured carbon nanotube-paper composites for wearable applications

The uniqueness of eyes, facial geometry, and gaze direction makes eye tracking a very challenging technological pursuit. Although camera-based eye-tracking systems are popular, the obtrusiveness of their bulky equipment along with their high computational cost and power consumption is considered problematic for wearable applications. Noncontact gaze monitoring using capacitive sensing technique has been attempted but failed due to low sensitivity and parasitic capacitance. Here, we study the interaction between a novel capacitive sensor and eye movement for wearable eye-tracking. The capacitive sensors are made of a pair of asymmetric electrodes; one comprising carbon nanotube-paper composite fibers (CPC) and the other being a rectangular metal electrode. The interaction between the asymmetric sensor and a spherical object mimicking an eyeball is analyzed numerically. Using a face simulator, both single- and differential capacitive measurements are characterized with respect to proximity, geometry, and human body charge. Using a prototype eye tracker, multiple sensor locations are studied to determine the optimal configurations. The capacitive responses to vertical and horizontal gaze directions are analyzed in comparison to those of a commercial eye tracking system. The performance is demonstrated for sensitive eye-movement tracking, closed-eye monitoring, and human-machine interface. This research has important implications for the development of capacitive, wearable eye trackers, which can facilitate fields of human-machine interface, cognitive monitoring, neuroscience research, and rehabilitation.

Magnetic suction compression-type gap sensor based on fiber Bragg grating sensing

PurposeA fiber Bragg grating (FBG) sensor was designed to measure the door gap of automobile bodies.Design/methodology/approachThe gap sensor was designed through a combination of the sliding wedge and cantilever beam, involving a magnetic force installation and arc structure of the force transmission point. Moreover, the sliding block adopted an anti-magnetic and wear-resistant material and the temperature compensation of the two FBGs was conducted. The magnetic force and contact stress of the sensor were examined to ensure that the sensor exhibited a certain magnetic attraction force and fatigue life. The performance of the gap sensor was examined experimentally.FindingsThe sensor could measure gaps with dimensions of 5 mm to 11 mm, with a sensitivity and measurement accuracy of 150.9 pm/mm and 0.0063% F.S., respectively. Moreover, the sensor exhibited a small gap sensitivity, with a repeatability error of 0.15%, anti-creep properties and magnetic interference abilities.Originality/valueThe sensor is compact and easy to install, as well as use for multiple sensor locations, with a maximum size of 43 mm, a mass of 26 g and installation type of magnetic suction. It can be used for high-precision static and dynamic measurements of the door inner clearance with a resolution of 0.013 mm to improve the efficiency of internal clearance on-line analysis and assembly quality inspection.

Calibration and validation of the dynamic response of two slab track models using data from a full-scale test rig

For the development of accurate and reliable simulation models, the procedure of calibration and validation against measurement data is essential. In this paper, a finite element model and a waveguide finite element model of a slab track are calibrated and validated against hammer impact measurement data from a full-scale test rig. The finite element model is three-dimensional, where the rails are modelled as Rayleigh–Timoshenko beams and the concrete slab and support layer are modelled using linear shell elements. In the waveguide finite element model, a constant track cross-section described by two-dimensional finite elements is assumed, and the vibration in the direction perpendicular to the cross-section is described by propagating waves that are decaying exponentially. Measured frequency response functions (FRFs) are compared with the corresponding calculated FRFs from the two modelling approaches. The calibration is conducted in two steps using (i) a parameter study and (ii) a genetic algorithm. For multiple excitation positions and sensor locations, both calibrated models capture the trend of the Single-Input Multiple-Output measurements with rather small deviations compared to the overall dynamic range. This implies that both models can successfully represent the dynamic response of the test rig and can be considered as validated.

Applying Bayesian Optimization for Calibration of Transportation Simulation Models

The parameters of a transportation simulation model need to pass through a careful calibration process to ensure that the model’s output is as close as possible to the actual system. Owing to the computationally expensive and black-box nature of a simulation model, there is a need for robust and efficient calibration algorithms. This paper proposes a Bayesian optimization framework for the high-dimensional calibration problem of transportation simulation models. Bayesian optimization uses acquisition functions to determine more promising values for future evaluation, instead of relying on local gradient approximations. It guarantees convergence to the global optimum with a reduced number of evaluations, therefore is very computationally efficient. The proposed algorithm is applied to the calibration of a simulation network coded in simulation of urban mobility (SUMO), an open-source microscopic transportation simulation platform, and compared with a well-known method named simultaneous perturbation stochastic approximation (SPSA). To assess the calibration accuracy, speed distributions obtained from the two models calibrated using these two different methods are compared with the observation. For both the Bayesian optimization and SPSA results, the simulated and observed distributions are validated to be from the same distribution at a 95% confidence level for multiple sensor locations. Thus, the calibration accuracy of the two approaches are both acceptable for a stochastic transportation simulation model. However, Bayesian optimization shows a better convergence and a higher computational efficiency than SPSA. In addition, the comparative results of multiple implementations validate its robustness for a noisy objective function, unlike SPSA which may sometimes get stuck in a local optimum and fail to converge in a global solution.

Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods.

The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain.

Non-stationary source localization using direction of arrival measurements from multiple in-air vector sensors

Vector sensors utilize a combination of pressure sensors, particle velocity sensors, or both, to determine the acoustic intensity magnitude and direction pointing toward an acoustic source. This acoustic intensity vector is often referred to as the Direction of Arrival (DOA). By combining DOA information from multiple vector sensor measurement locations, a sound source may be instantaneously localized. The majority of vector sensor research has been conducted for underwater applications. A few studies of in-air vector sensors, which utilize multiple microphones, have been conducted; however, the majority of them study stationary sound sources in a laboratory environment or non-real-world settings. The focus of this paper is to study in-air vector sensor capabilities when sensing non-stationary mechanical noise sources—specifically ground vehicles—in a non-laboratory environment where ambient noise may be present. The DOA measurements at multiple vector sensor locations are used to test the acoustic source localization potential for this method. Vector sensors utilize a combination of pressure sensors, particle velocity sensors, or both, to determine the acoustic intensity magnitude and direction pointing toward an acoustic source. This acoustic intensity vector is often referred to as the Direction of Arrival (DOA). By combining DOA information from multiple vector sensor measurement locations, a sound source may be instantaneously localized. The majority of vector sensor research has been conducted for underwater applications. A few studies of in-air vector sensors, which utilize multiple microphones, have been conducted; however, the majority of them study stationary sound sources in a laboratory environment or non-real-world settings. The focus of this paper is to study in-air vector sensor capabilities when sensing non-stationary mechanical noise sources—specifically ground vehicles—in a non-laboratory environment where ambient noise may be present. The DOA measurements at multiple vector sensor locations are used to test the acoustic sourc...

A two-step weighted least-squares method for joint estimation of source and sensor locations: A general framework

It is well known that the Two-step Weighted Least-Squares (TWLS) is a widely used method for source localization and sensor position refinement. For this reason, we propose a unified framework of the TWLS method for joint estimation of multiple disjoint sources and sensor locations in this paper. Unlike some existing works, the presented method is based on more general measurement model, and therefore it can be applied to many different localization scenarios. Besides, it does not have the initialization and local convergence problem. The closed-form expression for the covariance matrix of the proposed TWLS estimator is also derived by exploiting the first-order perturbation analysis. Moreover, the estimation accuracy of the TWLS method is shown analytically to achieve the Cramér-Rao Bound (CRB) before the threshold effect takes place. The theoretical analysis is also performed in a common mathematical framework, rather than aiming at some specific signal metrics. Finally, two numerical experiments are performed to support the theoretical development in this paper.

Sensor‐aware Normal Estimation for Point Clouds from 3D Range Scans

AbstractNormal vectors are essential for many point cloud operations, including segmentation, reconstruction and rendering. The robust estimation of normal vectors from 3D range scans is a challenging task due to undersampling and noise, specially when combining points sampled from multiple sensor locations. Our error model assumes a Gaussian distribution of the range error with spatially‐varying variances that depend on sensor distance and reflected intensity, mimicking the features of Lidar equipment. In this paper we study the impact of measurement errors on the covariance matrices of point neighborhoods. We show that covariance matrices of the true surface points can be estimated from those of the acquired points plus sensor‐dependent directional terms. We derive a lower bound on the neighbourhood size to guarantee that estimated matrix coefficients will be within a predefined error with a prescribed probability. This bound is key for achieving an optimal trade‐off between smoothness and fine detail preservation. We also propose and compare different strategies for handling neighborhoods with samples coming from multiple materials and sensors. We show analytically that our method provides better normal estimates than competing approaches in noise conditions similar to those found in Lidar equipment.

Structural Damage Detection Using Vibration Response Through Cross-Correlation Analysis: Experimental Study

A detection scheme to localize and quantify multiple damages in structures is proposed. At first, acceleration response histories at multiple sensor locations are collected experimentally from damaged and undamaged structures. Structures with single as well as multiple damages have been considered. The cross correlation between the acceleration histories at each sensor location is computed. The peak amplitudes of normalized cross-correlation outputs are used to form the damage indicators. The traditional threshold-based method of detecting damages from these damage indicators has been observed to be inadequate for the detection of multiple damages. Here, the drawback of threshold-based damage-detection method is overcome by using an artificial neural network. The accuracy of the damage-detection scheme has been shown for different damage cases.

Non-destructive testing based on vibrations in the low to mid-frequency range

Recently, it has been shown that a Time Reversal MUltiple Signal Classification (TR-MUSIC) algorithm can be employed to detect defects in samples, which are both challenging in terms of material and in terms of geometrical complexity. This can be achieved by lowering the detection frequency as compared to most other TR and TR-MUSIC applications. In this case, the method operates in a low to mid-frequency range, where accurate models are still realizable. The method relies on the measurement of the frequency response function (FRF) between multiple excitation and sensor locations. These are gathered in a matrix, which then is decomposed in its singular vectors, serving as input for a MUSIC algorithm. In order to improve the applicability of this method, it is shown how to adapt the algorithm, in order to reduce the number of excitation locations or the number of sensors significantly. This results in a considerable speed-up for the application of this non-destructive testing strategy. Furthermore, it is investigated numerically how the robustness of the detection result under the influence of random measurement errors behaves if the number of excitation/sensor locations is reduced. Additionally, the dependency between, the decrease of the number of excitation/sensor locations and the types of defects, which can be detected is studied.

Detection and monitoring of flexural cracks in reinforced concrete beams using mounted smart aggregate transducers

Previous studies have successfully demonstrated the capability and reliability of the use of Smart Aggregate (SA) transducers to monitor reinforced concrete (RC) structures. However, they mainly focused on the applications of embedded SAs to new structural members, while no major attention was paid to the monitoring of existing RC members using externally mounted SAs. In this paper, a mounted SA-based approach is proposed for a real-time health monitoring of existing RC beams. The proposed approach is verified through monitoring of RC beams under flexural loading, on each of which SA transducers are mounted as an actuator and sensors. The experimental results show that the proposed SA-based approach effectively evaluates the cracking status of RC beams in terms of the peak of power spectral density and damage indexes obtained at multiple sensor locations. It is also shown that the proposed sensor system can also capture a precautionary signal for major cracking.

Application of machine learning techniques to predict anomalies in water supply networks

Methods to improve the operational efficiency of a water supply network by early detection of anomalies are investigated by making use of the data streams from multiple sensor locations within the network. The water supply network is a demonstration site of Vitens, a Dutch water company that has several district metering areas where flow, pressure, electrical conductance and temperature are measured and logged online. Three different machine learning approaches are tested for their feasibility to detect anomalies. In the first approach, day-dependent support vector regression (SVR) models are trained for predicting the measurement signals and compared to straightforward models using mean and median estimates, respectively. Using SVRs or the averaged data as real-time pattern recognizers on all available signals, large leakages can be detected. The second approach utilizes adaptive orthogonal projections and reports an event when the number of hidden variables required to describe the streaming data to a user-defined degree (energy-level threshold) increases. As a third approach, (unsupervised) clustering techniques are applied to detect anomalies and underlying patterns from the raw data streams. Preliminary results indicate that the current dataset is too limited in the amount of events and patterns to harness the potential of these techniques.

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.

Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types.

Comparison of Maximal Lingual Pressure Generation During Isometric Gross and Fine Sensorimotor Tasks in Healthy Adults

ObjectivesTo (1) compare 2 distinct isometric lingual press tasks, fine sensorimotor versus gross sensorimotor, at multiple sensor locations in relation to age and sex; and (2) provide a normative data set using a lingual-strengthening device. DesignCohort study. SettingUniversity. ParticipantsHealthy men and women (N=71; age range, 21–82y) recruited from the community. InterventionsParticipants were stratified by age and sex and divided into 3 age groups. Participants completed, in random order, 2 isometric tasks: (1) fine sensorimotor: tongue press maximally and discreetly against each of 5 sensors; and (2) gross sensorimotor: tongue press maximally against all 5 sensors simultaneously. Main Outcome MeasuresPrimary outcome was maximum isometric pressure in hectopascals (hPa). Secondary outcomes were time to reach peak pressure (s) and pressure gradient (hPa/s). ResultsMaximum pressures were significantly lower in those of older age for both fine and gross sensorimotor lingual tasks (P<.01), with the front and back sensors showing the greatest decline (35% and 45%, respectively). Pressure differences between tasks (P=.0012) resulted in the fine sensorimotor task generating higher pressures at the front sensor for all age groups. However, the gross sensorimotor task generated faster maximum pressures at all sensor locations for all age groups. For both sensorimotor tasks, subjects of older age as a whole generated less steep pressure gradients (P<.001). ConclusionsAge-related decline in tongue strength is greater at the anterior and posterior tongue. Results indicate a simpler gross sensorimotor task may be more beneficial for targeting timing as a biomechanical parameter during therapy, and the fine sensorimotor task may be more beneficial for targeting strength.

Dynamic On-Chip Thermal Sensor Calibration Using Performance Counters

Numerous sensors are currently deployed in modern processors to collect thermal information for fine-grained dynamic thermal management (DTM). Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration cost for thermal sensors can be prohibitively high as the number of on-chip sensors increases. In this paper, a model which is suitable for online calculation is employed to estimate the temperatures of multiple sensor locations on the silicon die. The estimated sensor and actual sensor thermal profile show a very high similarity with correlation coefficient ${\sim}{\rm 0.9}$ for most tested benchmarks. Our calibration approach combines potentially inaccurate temperature values obtained from two sources: temperature readings from thermal sensors and temperature estimations using system performance counters. A data fusion strategy based on Bayesian inference, which combines information from these two sources, is demonstrated along with a temperature estimation approach using performance counters. The average absolute error of the corrected sensor temperature readings is ${ and the standard deviation of error is less than ${ for tested benchmarks.

Testing of a Bridge Weigh-in-Motion Algorithm Utilising Multiple Longitudinal Sensor Locations

Abstract A new bridge weigh-in-motion (WIM) algorithm is developed which makes use of strain sensors at multiple longitudinal locations of a bridge to calculate axle weights. The optimisation procedure at the core of the proposed algorithm seeks to minimise the difference between static theory and measurement, a procedure common in the majority of bridge WIM algorithms. In contrast to the single unique value calculated for each axle weight in common Bridge WIM algorithms, the new algorithm provides a time history for each axle based on a set of equations formulated for each sensor at each scan. Studying the determinant of this system of equations, those portions of the time history of calculated axle weights for which the equations are poorly conditioned are removed from the final reckoning of results. The accuracy of the algorithm is related to the ability to remove dynamics and the use of a precise influence line. These issues are addressed through the use of a robust moving average filter and a calibration procedure based on using trucks from ambient traffic. The influence of additional longitudinal sensor locations on the determinant of the system of equations is discussed. Sensitivity analyses are carried out to analyse the effect of a misread axle spacing or velocity on the predictions, and as a result, the algorithm reveals an ability to identify potentially erroneous predictions. The improvement in accuracy of the calculated axle weights with respect to common approaches is shown, first using numerical simulations based on a vehicle-bridge interaction finite-element model, and second using experimental data from a beam-and-slab bridge in Slovenia.

High-Frequency Underwater Acoustic Propagation in a Port Modeled as a Three-Dimensional Duct Closed at One End Using the Method of Images

A computer-efficient model for underwater acoustic propagation in a shallow, three-dimensional rectangular duct closed at one end has been developed using the method of images. The duct simulates a turning basin located in a port, surrounded with concrete walls, and filled with sea water. The channel bottom is composed of silt. The modeled impulse response is compared with the impulse response measured between 15 kHz and 33 kHz. Despite small sensor-position inaccuracies and an approximated duct geometry, the impulse response can be modeled with a relative echo magnitude error of 1.62 dB at worst and a relative echo location error varying between 0% and 4% when averaged across multiple measurements and sensor locations. This is a sufficient level of accuracy for the simulation of an acoustic communication system operating in the same frequency band and in shallow waters, as time fluctuations in echo magnitude commonly reach 10 dB in this type of environment.

A Skew-t space-varying regression model for the spectral analysis of resting state brain activity

It is known that in many neurological disorders such as Down syndrome, main brain rhythms shift their frequencies slightly, and characterizing the spatial distribution of these shifts is of interest. This article reports on the development of a Skew-t mixed model for the spatial analysis of resting state brain activity in healthy controls and individuals with Down syndrome. Time series of oscillatory brain activity are recorded using magnetoencephalography, and spectral summaries are examined at multiple sensor locations across the scalp. We focus on the mean frequency of the power spectral density, and use space-varying regression to examine associations with age, gender and Down syndrome across several scalp regions. Spatial smoothing priors are incorporated based on a multivariate Markov random field, and the markedly non-Gaussian nature of the spectral response variable is accommodated by the use of a Skew-t distribution. A range of models representing different assumptions on the association structure and response distribution are examined, and we conduct model selection using the deviance information criterion. (1) Our analysis suggests region-specific differences between healthy controls and individuals with Down syndrome, particularly in the left and right temporal regions, and produces smoothed maps indicating the scalp topography of the estimated differences.