Small Number Of Sensors Research Articles

Vibration-Based Health Monitoring of an Existing Truss Bridge Using Energy-Based Damping Evaluation

This paper proposes an analytical framework for vibration-based structural health monitoring (SHM) by introducing an energy-based damping evaluation (EBDE). The damage detection by the proposed EBDE was carried out by estimating the contribution of modal damping ratios from different structural elements utilizing experimentally identified modal damping ratios, and estimating modal strain and modal potential energies from an updated finite-element (FE) model of the structure under consideration. Model updating was performed using modal frequencies and mode shapes that are generally not sensitive to local damage. The advantage of using damping as a damage indicator is that the damping change in global modes affected by the local damage can be identified with a small number of sensors. A previous study reported that the studied bridge with damage at the local diagonal member showed a significant increase in the damping of global vibration mode of the structure. The present study utilized the EBDE to identify the cause of the modal damping increase by observing the change in the contribution from different structural elements on the modal damping ratios.

Damage localization based on the reconstructed Green’s function from a diffuse noise field on a thin rectangular aluminum plate

The extracted Green’s function from ambient noise cross-correlation is applied to localize a damage on an aluminum plate using a passive sensor array. Damage was investigated by drilling holes at various locations with different sizes. The localization process uses a subtracted Green’s function of an undamaged plate from that of a damaged plate, which can reveal the scattered wave from the damage boundary. Localization algorithms used in active structural health monitoring, including the time of arrival, the time difference of arrival, the energy arrival and the Rayleigh maximum likelihood estimation [Flynn et al., Proc. R. Soc. Lond. A. Math. Phys. Sci. (2011)] methods are applied to the subtracted signal according to the number of sensors and that of noise sources. The subtracted signals are band pass filtered with various center frequency resulting in multiple images which are used in image fusion to increase the accuracy of localization [Michaels et al., Wave motion. (2007)]. The performance of the algorithms demonstrates the efficacy of the passive sensing-based localization method even when using small number of sensors and sources.

Exact and approximate route set generation for resilient partial observability in sensor location problems

Sensor positioning is a fundamental problem in transportation networks, as the location of sensors strongly determines how traffic flows are observable and hence manageable.This paper aims to develop a methodology to determine sensor locations on a network such that an optimal trade-off solution is found between the amount of sensors installed and the resilience of the sensor set.In particular, we propose exact and heuristic solutions for identifying the optimal route sets such that no other route would include any additional information for finding optimal full and partial observability solutions. This is an important contribution to sensor location problems, as route-based link flow inference problems have non-unique solutions, strongly depending on the used link-route information.The properties of the new methodology are analyzed and illustrated through different case studies, and the advantages of the algorithms are quantified both for full and for partial observability solutions. Due to the route sets found by our approach, we are able to find full observability solutions characterized by a small number of sensors, while yet being efficient also in terms of partial observability. We perform validation tests on both small and real-life sized network instances.

Source localization with three-dimensional sound intensity probe with high precision

When an array module measuring three-dimensional sound intensity is employed for detecting the sound source, a compact space usage and small number of sensors are advantageous than the other source localization methods. However, because of severe bias errors, it has not been popular. We analyze the major sources of bias estimation error and seek for the compensation method. Spectral bias error is due to the reflected signal from the environment, which is proportional to the difference of distance between direct and reflective paths. Spatial bias error is due to the inhomogeneous directivity of the intensity module stemming from discrete arrangement of sensors on the hypothetical sphere surrounding sensors. Simulation with changing the source direction by 1 deg. in spherical angle can generate an error map for all incidence angles. A measurement is conducted using a tetrahedral intensity module with 30 mm spacing for the compensation of errors. Low pass filtering of the cross spectral density function is used for the spectral bias error, and spherical error map is used for the directional bias error. By compensating such bias errors, it is shown that the localization errors of all bearing angles are less than 1° in an anechoic chamber when kd<1.1.

Development of real-time gait phase detection system for a lower extremity exoskeleton robot

In this paper, we develop an insole sensor system that can determine various dynamic models of a lower extremity exoskeleton. The study analyzed the kinematic model of exoskeleton robot for lower limb that changes according to the gait phase detection of a human. Based on the ground reaction force (GRF) that is generated when walking, the sensing type, location, and the number of sensors were selected to proceed on insole sensor development. Using the COP, an algorithm was developed that is capable of detecting gait phase with small number of sensors. An experiment was conducted to evaluate the developed insole sensor system and the gait phase detection algorithm.

Using High-Resolution Simulations to Quantify Underestimates of Tornado Intensity from In Situ Observations

Abstract Large-eddy simulations are used to produce realistic, high-resolution depictions of near-surface winds in translating tornadoes. The translation speed, swirl ratio, and vertical forcing are varied to provide a range of vortex intensities and structural types. Observation experiments are then performed in which the tornadoes are passed over groups of simulated sensors. Some of the experiments use indestructible, error-free anemometers while others limit the range of observable wind speeds to mimic the characteristics of damage indicators specified in the enhanced Fujita (EF) scale. Also, in some of the experiments the sensors are randomly placed while in others they are positioned in regularly spaced columns perpendicular to the vortex tracks to mimic field project deployments. Statistical analysis of the results provides quantitative insight into the limitations of tornado intensity estimates based on damage surveys or in situ measurements in rural or semirural areas. The mean negative bias relative to the “true” global maximum 3-s gust at 10 m AGL (the standard for EF ratings) exceeds 10 m s−1 in all cases and 45 m s−1 in some cases. A small number of sensors are generally sufficient to provide a good approximation of the running time-mean maximum during the period of observation, although the required spatial resolution of the sensor group is still substantially higher than that previously attained by any field program. Because of model limitations and simplifying assumptions, these results are regarded as a lower bound for tornado intensity underestimates in rural and semirural areas and provide a baseline for further inquiry.

Optimisation of energy efficient cellular learning automata algorithm for heterogeneous wireless sensor networks

Wireless sensor networks is an effective sensing network consisting of a large number of small sensors and small embedded devices each with sensing, computation and communication capabilities for gathering data in various environments. Energy consumption is considered to be an important issue in the design of wireless sensor networks. To overcome the above limitation, efficient method like cellular learning automata (CLA) and heterogeneous-hybrid energy efficient distributed (H-HEED) technique have been used in distributed dynamic clustering networks. The existing method will be the cellular learning automata in which cluster heads will be selected through several stages by considering various parameters with homogeneous nodes. The proposed method selects the cluster head in a similar way and based on the residual energy of the nodes with heterogenous nodes. Their performance is observed using NS2 simulator and comparison has been made to find the best efficient method.

Using an infrasonic method to monitor the destruction of glaciers in Arctic conditions

We study the application of an infrasonic method to detect infrasonic acoustic emission caused by the destruction of glaciers in the Arctic. We consider the main approaches and methods for automatic signal detection from the data of infrasonic microarrays from the viewpoint of their practical use in conditions of frequent and significant variations in the noise level characteristics of the Arctic coast. We propose a novel method for the automatic detection of infrasonic events based on representation of a plane wave signal and adaptive estimation of the noise level. The method makes it possible to detect signals with a small number of sensors (up to three) in the specific conditions of the Arctic coast. We present the results of infrasonic monitoring of the destruction of Icefjord outlet glaciers (Spitsbergen archipelago) carried out by the Kola Branch of the RAS Geophysical Survey in 2011–2012.

Acoustic emission monitoring of grouted splice sleeve connectors and reinforced precast concrete bridge assemblies

Acoustic emission (AE) monitoring was performed for two types of grouted splice sleeve (GSS) connectors used in reinforced precast concrete structures. The GSS connectors were subjected to monotonic tension tests. AE assessment of the GSS connectors demonstrated that AE events and event rates could be used to identify failure modes which were steel bar fracture for the first type of connectors and steel bar pullout for the second type. The AE event history curves can be taken as representative of each GSS connector type. AE monitoring was also performed for reinforced precast concrete column-to-footing and column-to-pier cap bridge assemblies utilizing GSS connectors. The bridge assemblies were subjected to quasi-static cyclic loads simulating earthquakes. The failure modes of the GSS connectors were also observed in the cyclic tests of the bridge assemblies. AE monitoring was able to identify column concrete cracking and full development of concrete cracks using a small number of sensors. The AE method was also able to identify rotation of the column for the column-to-footing assembly after fracture of column longitudinal bars.

Control of the Nonlinear Wave-Type Dynamics Using the Derivative-Free Nonlinear Kalman Filter

The objective of the paper is to develop a pointwise control method for a 1D nonlinear wave equation and a filtering approach for estimating the dynamics of such a system from measurements provided from a small number of sensors. It is shown that the numerical solution of the associated partial differential equation results into a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system in the linear canonical (Brunovsky) form is obtained. This transformation enables to obtain estimates about the state vector of the system through the application of the standard Kalman Filter recursion. For the local subsystems, into which the nonlinear wave equation is decomposed, it becomes possible to apply pointwise state estimation-based feedback control. The efficiency of the proposed filtering and control approach for nonlinear systems described by 1D partial differential equations of the wave type (e.g. sine-Gordon PDE) is confirmed through simulation experiments. It is shown that, by applying feedback control, the nonlinear wave-type dynamics can be made to track any reference setpoint.

Field Verification of Simplified Bridge Weigh-in-Motion Techniques

AbstractThis study addresses the feasibility of using a single-span bridge as a weigh-in-motion (WIM) tool to quantify the gross vehicle weights (GVWs) of trucks inexpensively with a small number of sensors and without using axle detectors. Field testing was performed on an interstate without any lane closures. Four preweighed trucks with different axle configurations traveled over a bridge at three different speeds and in two separate lanes. Measured strain data were used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison was made between calculated and actual measured static weights, and between the calculated and specified speeds of the trucks. In addition to field testing, a finite-element (FE) model of the tested bridge was created and calibrated based on the measured strains at different locations. This calibrated FE model enabled the acquisition of the influence values for the bridge at any location (influence surface). Ten diffe...

Compressive Hyperspectral Imaging via Sparse Tensor and Nonlinear Compressed Sensing

Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.

Human Activity Recognition by Combining a Small Number of Classifiers.

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

New Potentiometric Electronic Tongue for Analysing Teas and Infusions

AbstractIn this work, an electronic tongue (ET) system is developed for the discrimination of infusions and teas made from different herb leaves. The ET is based on an array of four non‐specific potentiometric sensors plus principal component analysis (PCA) data processing. The four sensors used are a cation‐selective electrode and an anion‐selective electrode, containing both a plasticized polymeric membrane with different ionic additives, a commercial pH electrode and a redox electrode. These provide simple, rapid and very reproducible potentiometric responses after direct exposure to infusions and teas, which do not need any pre‐treatment. A radar plot of potentiometric values for each sensor, obtained after sixty seconds of array exposure to the samples, revealed a differentiated response for each sensor and infusion. This was especially marked for anion‐selective and redox electrodes. The PCA results of the potentiometric signals point to certain trends that can be used for classification purposes. Although a small number of sensors is used in the developed ET, interesting classification functions have been achieved, which will be exploited in the near future with a larger number of samples. The concept of an ET formed by a reduced number of potentiometric sensors is starting to spread to other types of beverages.

AN EXACTLY-SOLVABLE QUASISTATIC ELECTRICITY INVERSE PROBLEM: RETRIEVAL OF THE COMPLEX PERMITTIVITY OF A CYLINDER TAKING ACCOUNT OF NUISANCE PARAMETER UNCERTAINTY

—This study concerns the 2D inverse problem of the retrieval, using external field data, of either one of the two physical parameters, constituted by the real and imaginary parts of the permittivity, of a z-independent cylindrical dielectric specimen subjected to an external, z-independent, quasistatic electric field. Six other parameters enter into the inverse problem. They are termed nuisance parameters because: 1) they are not retrieved during the inversion and 2) uncertainty as to their actual values can adversely affect the accuracy of the retrieval of the permittivity. This inverse problem is shown to have an exact, mathematically-explicit, solution, both for continuous and discrete input data, whose properties, with respect to the various nuisance parameter uncertainties, are analyzed for noiseless data. It is found that: a) optimal inversion requires data registered at only a small number of sensors, b) the inverse solution, satisfying pre-existing physical constraints, exists and is unique. Moreover, the inverse solution is shown to be unstable with respect to three nuisance parameter uncertainties, the consequence of which is large retrieval inaccuracy for small nuisance parameter uncertainties, acting either individually or in combination.

A Novel Energy-Aware Approach for Locating Leaks in Water Pipeline Using a Wireless Sensor Network and Noisy Pressure Sensor Data

We propose a novel energy-aware approach to detect a leak and estimate its size and location in a noisy water pipeline using least-squares and various pressure measurements in the pipeline network. The novelty in our work hinges on the fusion of the duty-cycling (DC) and data-driven (DD) strategies, both well-known techniques for energy reduction in a wireless sensor network (WSN). To maximize the information gain and minimize the energy consumed by the WSN, we first study the effects of (a) various levels of sensor measurement uncertainty and (b) the use of the smallest possible number of pressure sensors on the overall accuracy of our approach. Using the DD strategy only, a noisy environment, and a small number of sensors, the performance of our scheme shows that, for small leak sizes, the estimation error in both leak location and size becomes unacceptably high. Next, using as few sensors as possible for an acceptable accuracy, we fused the DD strategy with the DC one to minimize the sensing, processing, and communication energies. The fusion approach yielded a better performance with significant energy saving, even in noisy environments. EPANET was used to model the pipeline network and leak and MATLAB to implement, analyze, and evaluate our fusion approach.

Feedback control and Kalman Filtering of nonlinear wave dynamics

The paper proposes a pointwise control method for the 1D nonlinear wave equation and a filtering approach for estimating the dynamics of such a system from measurements provided by a small number of sensors. It is shown that the numerical solution of the associated partial differential equation results into a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system in the linear canonical (Brunovsky) form is obtained. This transformation enables to obtain estimates about the state vector of the system through the application of the standard Kalman Filter recursion. For the local subsystems, into which the nonlinear wave equation is decomposed, it becomes possible to apply pointwise state estimation-based feedback control. The efficiency of the proposed filtering and control approach for nonlinear systems described by 1D partial differential equations of the wave type (e.g. sine- Gordon PDE) is confirmed through simulation experiments.

Exploitation of the Reverberant Propagation of Elastic Waves in Structures: Towards a Concept of Low-resource SHM Sensor Network

Actual implementation of an efficient SHM system is necessarily hampered by the constraints of power-consumption and intrusive- ness (weight, size, in-service integration) of sensors. In the field of ultrasound-based SHM, conventional methods rely on relatively powerful acoustic sources synchronized with the sensors, and exploit only the first propagated (ballistic) wavepackets. The aim of this paper is to present possible techniques to exploit the whole complexity of reverberation signals, in order to extract the maxi- mum information from limited hardware, software, or power resources. A first aspect is the extraction of statistical properties of the codas of multiply-reflected signals, which can be used to estimate structural properties from a small number of sensors. In this technique, the required signal processing is relatively light and synchronization between the acquisition channels is not necessary. A second aspect is concerned with the possibility of using ambient acoustic sources, naturally present for example in transportation applications, instead of artificial power-consuming ultrasound sources.

Application Of Guided Wave Propagation In Diagnostics Of Steel Bridge Components

AbstractEarly detection of potential defects and identification of their location are necessary to ensure safe, reliable and long-term use of engineering structures. Non-destructive diagnostic tests based on guided wave propagation are becoming more popular because of the possibility to inspect large areas during a single measurement with a small number of sensors. The aim of this study is the application of guided wave propagation in non-destructive diagnostics of steel bridges. The paper contains results of numerical analyses for a typical railway bridge. The ability of damage detection using guided Lamb waves was demonstrated on the example of a part of a plate girder as well as a bolted connection. In addition, laboratory tests were performed to investigate the practical application of wave propagation for a steel plate and a prestressed bolted joint.

Electronic noses for environmental monitoring applications.

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments' proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures.