Optimal Sensor Location Research Articles

센서최적배치 기법에 의한 원통형 구조물의 진동장 예측

This study is concerned with the estimation of vibration-field of a cylindrical structure by modal expansion method(MEM). MEM is a technique that identifies modal participation factors using some of vibration signals and natural modes of the structure: The selection of sensor locations has a big influence on predicted vibration results. Therefore, this paper deals with four optimal sensor placement(OSP) methods, EFI, EFI-DPR, EVP, AutoMAC, for the estimation of vibration field. It also finds optimal sensor locations of the cylindrical structure by each OSP method and then performs MEMs. Predicted vibration results compared with reference ones obtained by forced response analysis. The standard deviations of errors between reference and predicted results were also calculated. It is utilized to select the most suitable OSP method for estimation of vibration field of the cylindrical structure.

Optimal sensor placement in timber structures by means of a multi-scale approach with material uncertainty

SUMMARY This paper investigates the optimal location of sensors in a timber structure when parametric uncertainty is considered in the microstructure of the material. In order to describe the proposed methodology, a timber beam is chosen for our numerical simulations. A classical sensor location methodology based on the Fisher information matrix is employed. Within a finite element framework, a homogenization-based multi-scale approach is adopted to model the constitutive response of timber, along with stochastic properties in the definition of the material. Nevertheless, by considering uncertainty in the micromechanical properties of wood, a significant computational cost is added to the solution of a large set of realizations represented by expensive multi-scale analyses. In order to tackle this high cost, we build a statistical approximation to the output of the computer model, known as a Bayesian emulator. Following this strategy, three micromechanical parameters are chosen to study their influence on the selection of different configurations of sensor placement. The optimal location of sensors is assessed with three different criteria: the Fisher matrix determinant, the modal assurance criterion error and the condition number. Furthermore, the robustness of this configuration is investigated in the presence of noise. Copyright © 2014 John Wiley & Sons, Ltd.

Near-Optimal Sensor Placement for Linear Inverse Problems

A classic problem is the estimation of a set of parameters from measurements collected by only a few sensors. The number of sensors is often limited by physical or economical constraints and their placement is of fundamental importance to obtain accurate estimates. Unfortunately, the selection of the optimal sensor locations is intrinsically combinatorial and the available approximation algorithms are not guaranteed to generate good solutions in all cases of interest. We propose FrameSense, a greedy algorithm for the selection of optimal sensor locations. The core cost function of the algorithm is the frame potential, a scalar property of matrices that measures the orthogonality of its rows. Notably, FrameSense is the first algorithm that is near-optimal in terms of mean square error, meaning that its solution is always guaranteed to be close to the optimal one. Moreover, we show with an extensive set of numerical experiments that FrameSense achieves state-of-the-art performance while having the lowest computational cost, when compared to other greedy methods.

Study on Statistical Prediction and Design Method for Indoor Thermal Environment

The indoor thermal environment is affected by many heat factors such as occupants′ activities and outdoor climate changes. Because each of them varies asynchronously, the heat factors′ variation shows a nonsingle degree of freedom overall. Moreover, the heat factors′ impact on space is not uniform. Therefore, there always exist spatial distributions and temporal variations. In practice, the number of degrees of freedom of an air-conditioning system is usually limited and less than that of the object zone. The control performance of an air-conditioning system designed under a perfect mixing method cannot be satisfactory because the spatial distribution is neglected. For energy conservation and thermal comfort improvement, it is necessary to propose an air-conditioning system design method which can positively use the spatial distribution and temporal variation of the indoor thermal environment. In this study, methods for indoor temperature fluctuation analysis and optimization of air-conditioner sensor location are proposed. To represent the temperature field structure, an index called CRI is integrated. With this method, the spatial distribution and temporal variation can be calculated immediately and analyzed statistically; furthermore, the indoor temperature and outdoor climate′s correlation can be concisely visualized. A case study is conducted to verify the validity of this method.

A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification

We present an efficient method for computing A-optimal experimental designs for infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs). Specifically, we address the problem of optimizing the location of sensors (at which observational data are collected) to minimize the uncertainty in the parameters estimated by solving the inverse problem, where the uncertainty is expressed by the trace of the posterior covariance. Computing optimal experimental designs (OEDs) is particularly challenging for inverse problems governed by computationally expensive PDE models with infinite-dimensional (or, after discretization, high-dimensional) parameters. To alleviate the computational cost, we exploit the problem structure and build a low-rank approximation of the parameter-to-observable map, preconditioned with the square root of the prior covariance operator. The availability of this low-rank surrogate, relieves our method from expensive PDE solves when evaluating the optimal experimental design objective function, i.e., the trace of the posterior covariance, and its derivatives. Moreover, we employ a randomized trace estimator for efficient evaluation of the OED objective function. We control the sparsity of the sensor configuration by employing a sequence of penalty functions that successively approximate the $\ell_0$-``norm''; this results in binary designs that characterize optimal sensor locations. We present numerical results for inference of the initial condition from spatiotemporal observations in a time-dependent advection-diffusion problem in two and three space dimensions. We find that an optimal design can be computed at a cost, measured in number of forward PDE solves, that is independent of the parameter and sensor dimensions. Moreover, the numerical optimization problem for finding the optimal design can be solved in a number of interior-point quasi-Newton iterations that is insensitive to the parameter and sensor dimensions. We demonstrate numerically that $\ell_0$-sparsified experimental designs obtained via a continuation method outperform $\ell_1$-sparsified designs.

Optimal Sensor Placement for Stay Cable Damage Identification of Cable-Stayed Bridge under Uncertainty

Large cable-stayed bridges utilize hundreds of stay cables. Thus, placing a sensor on every stay cable of bridges for stay cable damage identification (SCDI) is costly and, in most cases, not necessary. Optimal sensor placement is a significant and critical issue for SCDI. This paper proposes the criteria for sensor quantity and location optimization for SCDI on the basis of the concept of damage identification reliability index ( DIRI) under uncertainty. Random elimination (RE) algorithm and heuristic random elimination (HRE) algorithm are proposed to solve the sensor quantity optimization calculation problem. Multistage global optimization (MGO) algorithm is also proposed to solve the sensor location optimization calculation problem. A case study is conducted to evaluate and verify the criteria and algorithms. Results indicate that the HRE algorithm can provide better solution with less elapsed time than the RE algorithm can in some cases, and the MGO algorithm can meet the multistage criterion for sensor location optimization and give a satisfying optimized solution. Theoretical analysis and case study results confirm that the criteria are reasonable and suitable for optimal sensor placement for SCDI. The proposed algorithms are effective and efficient for practical optimization calculation.

Distributed k-Coverage Decision Scheme for System Deployment in Mobile Sensor Networks

K-coverage sensor deployment is an important issue in wireless sensor networks (WSNs) applications. This is because an efficient topology structure significantly affects the quality of service and lifetime of WSNs. In the present work, we study the sensor deployment problem and propose novel distributed decision schemes to guide sensor movement to achieve k-coverage deployment. In our schemes, the kth order Voronoi diagram is used to discover the regions that do not meet the k-coverage requirement. Two different movement strategies are designed to determine the optimal location of each mobile sensor through local topological information. Finally, simulation results validate the effectiveness and accuracy of the proposed schemes.

Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors

We report a new method to forecast power output from photovoltaic (PV) systems under cloudy skies that uses measurements from ground-based irradiance sensors as an input. This work describes an implementation of this forecasting method in the Tucson, AZ region where we use 80 residential rooftop PV systems distributed over a 50km×50km area as irradiance sensors. We report RMS and mean bias errors for a one year period of operation and compare our results to the persistence model as well as forecasts from other authors. We also present a general framework to model station-pair correlations of intermittency due to clouds that reproduces the observations in this work as well as those of other authors. Our framework is able to describe the RMS errors of velocimetry based forecasting methods over three orders of magnitude in the forecast horizon (from 30s to 6h). Finally, we use this framework to recommend optimal locations of irradiance sensors in future implementations of our forecasting method.

Temperature Sensor Allocation Strategy and Full Thermal Reconstruction for Microprocessors

On-chip thermal sensors are employed by dynamic thermal management techniques to measure runtime thermal behavior of microprocessors so as to prevent the on-set of high temperatures. The allocation and the placement of thermal sensors directly impact the effectiveness of the dynamic thermal management mechanisms. In this paper, we propose systematic and effective strategies for determining the optimal locations for temperature sensors based on thermal gradient analysis to provide the trade-off between hot spot estimation and full thermal reconstruction. Experimental results indicate the superiority of our techniques and confirm that our proposed methods are able to create a sensor distribution for a given microprocessor architecture.

Theoretical Research on Two Improved Optimal Sensor Placement Methods

Optimal sensor placement technique plays a key role in structural health monitoring and structural vibration control. Based on the advantages and disadvantages of effective independence (EI) and modal kinetic energy (MKE) methods, two improved optimal sensor placement methods which are Effective Independence - Average Acceleration Amplitude (EI-AAA) method and Effective Independence - Modal Kinetic Energy (EI-MKE) method are proposed in this paper. Firstly the formulas are deduced from modal expansion of multiple-input-multiple-output (MIMO) displacement frequency response function matrix. Then a computational simulation of steel cross beam structure has been implemented to demonstrate the feasibility of the two improved methods above. The obtained optimal sensor locations using the two improved methods are compared with those gained by EI method and MKE method. Finally six classical comparison criteria are employed to demonstrate the advantage and disadvantage of these four methods. The results showed that some innovations proposed in this paper are effective and reliable. The two improved optimal sensor placement methods (EI-AAA method and EI-MKE method) can not only make the truncated mode shapes as linearly independent as possible but also enable the measured modal kinetic energy to maintain the maximum value.

Robustness of optimal sensor placement under parametric uncertainty

This paper examines the influence of parametric uncertainties on the optimal sensor placement methodologies for the modal analysis of a truss bridge. Four classical sensor location methodologies are employed: two based on the Fisher information matrix and the other two based on energy matrix rank optimization. Young's modulus, mass density and cross sectional dimensions are considered as uncertain parameters. The independent and cumulative effects of these uncertain variables on the sensor placement are studied. The optimal locations of sensors under parametric uncertainty are assessed with three different criteria: the Fisher matrix determinant, the modal assurance criterion error and the condition number. Furthermore, the robustness of this configuration is investigated for the case of random and bias errors. The numerical results show the parametric uncertainties have significant influence on the optimal sensor configuration of a truss bridge.

Optimal locations of piezoelectric actuators and sensors for supersonic flutter control of composite laminated panels

The optimal active flutter control of supersonic composite laminated panels is studied using the distributed piezoelectric actuators/sensors pairs. The supersonic piston theory is used to calculate the unsteady aerodynamic pressure, and Hamilton’s principle with the assumed mode method is employed to develop the equation of motion of the structural system. The controllers are designed by the proportional feedback control method and the linear quadratic Gauss (LQG) algorithm. The optimal locations of the actuator/sensor pairs are determined by the genetic algorithm (GA). The aeroelastic properties of the structural system are mainly analyzed using the frequency-domain method. The time-domain responses of the structure are also computed using the Runge–Kutta method. The influences of ply angle on the flutter bound of the laminated panel with different length–width ratios are analyzed. The optimal design for the locations for different numbers of piezoelectric patches used in the proportional feedback control is carried out through the GA. Meanwhile, the control effects using different numbers of actuator/sensor pairs are investigated. The flutter suppression by the LQG algorithm is also carried out. The control effects using the two different controllers are compared. Numerical simulations show that the optimal locations obtained by the GA can increase the critical flutter aerodynamic pressure significantly, and the LQG algorithm is more effective in flutter suppression for supersonic structures than the proportional feedback controller.

OPTIMIZATION OF SEISMIC SENSOR LOCATIONS ALONG HIGHWAY LINKS

Optimum positioning of newly installing seismographs along highway lines is discussed. First, we introduce a method to estimate the spatial distribution of earthquake intensity under the condition that several observation data of earthquake intensity are provided at given sensor locations. We also define a spatial index which indicates the estimation error level of the spatial distribution of earthquake intensity of which value becomes zero at the sensor locations. The optimum sensor location problems can be formulated as a minimization problem of this index value which is a function of the sensor locations and the prior information of the special distribution of earthquake intensity in the target area. The design parameters are locations of the sensors. Genetic Algorithm (GA) is used for the optimization. After a brief introduction of real coded GA, optimum sensor locations along highway lines are determined as to minimize the spatial index value by using this GA code.

Numerical modelling of scattered Lamb waves through varied damage size in challenging geometry

Lamb waves have been accepted as a potential technology for damage detection. However, industry has not adopted the technology due to the fundamental nature of current studies. This article attempts to bring together several areas of knowledge to make a step towards adoption. Commonly, readings from damaged specimens are compared with baseline readings to establish damage. A quantitative approach (scatter calculation) is taken in this article to reveal areas or sensitivity and possible quantification of damage. This method is applied to displacement fields collected through laser vibrometry and numerical simulations. The numerical simulations are performed alongside to assess their usefulness and feasibility when paired with scatter calculations. Analysis of two specimens is presented here: an idealised flat plate and representative aircraft lower wing skin are progressively cut to investigate their response to damage. Despite similar crack-actuator placement in both specimens, the damage-sensitive areas are significantly impacted by geometry changes. This leads to a shift in optimal location of sensors and corresponding prospects of damage quantification.

Active control of axial and centrifugal fan noise

Both axial and centrifugal fans are used to cool information technology (IT) equipment. These fans generate noise that can be annoying to their users, particularly the tonal noise that can be radiated. Work has focused on developing a method to attenuate the tonal noise associated with both of these types of fans. A compact system is used, whereby control sources are placed in close proximity to the fan. A genetic algorithm has been implemented to determine optimal source configurations. The attenuation associated with some configurations is found to be much more sensitive to error than others. For a given configuration, by using a relatively simple point source model it becomes possible to identify optimal near-field error sensor locations, which results in a compact noise control solution that provides significant global attenuation of the radiated tonal noise. This paper will review progress that has been made to apply this method to both axial and centrifugal fans. Experimental results confirm that it is feasible to achieve significant global control using this method.

Optimal Planning of Distributed Sensor Layouts for Collaborative Surveillance

The use of a spatially distributed set of sensors has become a cost-effective approach to achieve surveillance coverage against moving targets. As more sensors are utilized in a collaborative manner, the optimal placement of sensors becomes critical to achieve the most efficient coverage. In this paper, we develop a numerical optimization approach to place distributed sets of sensors to perform surveillance against moving targets over extended areas. In particular, we develop a genetic algorithm solution to find spatial sensor density functions that maximize effectiveness against moving targets, where the surveillance performance of individual sensors is dependent on their absolute position in the region as well as their relative position to both the expected target(s) and any asset that is being protected. The density function representation of optimal sensor locations is shown to provide a computationally efficient method for determining sensor asset location planning. We illustrate the effective performance of this method on numerical examples based on problems of general area surveillance and risk-based surveillance in protection of an asset.

Optimized sensor placement for damage parameter estimation: Experimental results for a cracked plate

In this article, the optimum sensor and impact locations, for use in a damage identification experiment, are obtained using a hybrid genetic algorithm/steepest descent optimization method. Specifically, data from these optimum locations are used to identify the location, orientation, and size of a crack (termed the crack parameters) in a rectangular plate. The strain gage locations and orientations were selected in order (a) to maximize the difference between the model signal for a healthy plate and the model signal for a randomly damaged plate and (b) to minimize the cross-correlation among the signals measured by each of the gages. The latter requirement, in a sense, maximizes the uniqueness of the information measured from each sensor. The Bayesian model-based structural health monitoring identification technique, used to assess the crack parameters, was previously shown to be successful even for arbitrary sensor location/orientation and excitation location. It is shown here that thoughtful (optimized) sensor and excitation locations allow for improved estimates of the crack parameters. However, there is no substantial change in the width of the confidence intervals associated with these estimates.

Vehicular network sensor placement optimization under uncertainty

Uncertainty is one of the major factors that transportation system analysts and planners have to deal with when making transportation planning decisions. Finding the set of optimal sensor locations under uncertainty is a network design problem. This paper presents a nonlinear two-stage stochastic model. The first stage provides a sensor location plan to maximize the origin–destination (OD) flow coverage and link information gains, subject to a budgetary limitation, before considering any random events, while the recourse function associated with the second stage calculates the expected cost of vehicular flow changes, after random events occur. This novel two-stage stochastic bi-objective model simultaneously maximizes a weighted combination of link information gains and OD flow coverage to locate network passive point sensors. An iterative heuristic solution algorithm, hybrid Greedy Randomized Adaptive Search Procedure (HGRASP), is developed to find the near-optimal locations for this problem. The proposed methodology is tested on the Coordinated Highways Action Response Team network (Washington DC–Baltimore, Maryland corridor), in a mesoscopic traffic simulator. The results confirm the expectations that, under stochastic conditions, the sensor location plans obtained under the assumption of stochastic conditions result in better performance than plans developed for deterministic (normal) conditions. Also as expected, a greater number of optimally deployed sensors reduces demand uncertainty for both normal and incident conditions.

Lifting style and participant's sex do not affect optimal inertial sensor location for ambulatory assessment of trunk inclination

Trunk inclination (TI) is often used as a measure to quantify back loading in ergonomic workplace evaluation. The goal of the present study was to determine the effects of lifting style and participant's sex on the optimal inertial sensor (IS) location on the back of the trunk for the measurement of TI. Gold-standard TI, defined as the angle between the vertical and the line connecting the L5/S1 joint and the trunk center of mass, was measured using an optoelectronic system. Ten males and ten females performed experimental trials in which a box was lifted from floor level to a 75cm elevated surface. In each trial the box was lifted using four different styles: symmetric and asymmetric free-style lifts, a stoop lift and a squat lift. Trials were repeated for 13 IS locations between 10% and 40% of the distance from the sacrum to the seventh cervical spinous process (C7). For each participant and each IS location, the root-mean-square error (RMSE) between the gold standard TI and the ISTI was determined. A three-way repeated measures ANOVA analysis revealed no significant effects of the participant's sex on the RMSEs, but the main effects of lifting style and sensor location and their interaction were significant. Despite this significant interaction, a sensor location between 20% and 27.5% of the distance from the sacrum to C7 yielded the smallest RMSEs across all lifting styles. In conclusion, regardless of participant's sex or lifting style, the optimal IS location for the measurement of TI is at about 25% of the distance from the sacrum to C7.

Optimal Number and Location of Node-Based Sensors for Collection of Travel Time Data in Networks

Travel time prediction is highly useful in traffic management and planning. The prediction's accuracy relies on the accuracy of the travel time data used. Various sensors are being used to collect travel time data, including link-based and node-based sensors. Recently, a new method in the collection of travel time data was introduced: Bluetooth technology, used to detect Bluetooth devices in vehicles and determine the vehicles' travel times. Bluetooth sensors are generally node-based sensors. Despite the amount of literature available on sensor location problems, the use of node-based sensors to collect travel time data is rarely discussed. Projects that looked at the collection of travel time data with Bluetooth sensors prompted this study of the problem of sensor location in the installation of Bluetooth sensors and, more generally, node-based sensors. The goal of this study is to find the optimal number of node-based sensors and their optimal deployment locations in a network for the collection of highly reliable travel time data. Two formulations are proposed for modeling this problem. The formulations consider a new set of reliability factors. Through these formulations, the sensor location problem can be solved optimally for large networks. The proposed formulations are not restricted to Bluetooth sensors and can be applied to any node-based sensor location problem. Various case studies that use real-world networks are conducted to compare the results obtained from both the proposed formulations with the methodologies available in the literature. The findings of the case studies are reported in the paper.