Sensor Placement Method Research Articles

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

Sensor placement for RSSD-based localization: Optimal angular placement and sequential sensor placement

Energy-based localization has received great interest due to its low cost and simple implementation. It is well-known that sensor placement around the source plays a significant role in localization performance. This paper considers source localization based on the received signal strength difference (RSSD). Then, we propose two methods for sensor placement using maximization of the determinant of Fisher information matrix (FIM). The first one is based on the Gradient optimization method in which our optimization metric is a function of angular locations of all sensors, and the output of this method is optimal angular sensor separation which is called the optimal angular placement (OAP). In the second approach, we obtain the optimization metric as a function of single variable for each sensor in which the sensors are arranged in step-by-step manner, this method is called sequential sensor placement (SSP) in our study. At the end of this paper, simulation results reveal the ability of the proposed sensor placements.

Optimal Sensor Placement Method Considering the Importance of Structural Performance Degradation for the Allowable Loadings for Damage Identification

Considering the importance of damage for the structural performance and for decreasing the identification error, this paper proposes an optimal sensor placement method based on a weighted standard deviation norm (WSDN) index. The standard deviation of the identified damage parameters is solved using the series expansion theory and probabilistic method to quantify the effect of a measurement error on damage identification. The damage estimation weight (DEW) index, which can reflect the importance of each element in the structural capabilities, is established based on a performance-damage curve. A significant DEW for a specified element indicates that the element is important for the structure and that its identification error should be small. The WSDN index is obtained from the Hadamard product of the standard deviations (SDs) and DEWs. Thus, the identification error of the entire structure is measured using the weighting coefficient. The optimal sensor placement (OSP) procedure is performed by minimizing the WSDN index. The proposed method can clearly decrease the uncertainties of the identification results for the important elements. Other OSP criteria, including the condition number, information entropy, and standard deviation norm, which aim to decrease the identification error, are discussed in this paper for comparison with the proposed method. Two numerical examples and an experiment, which pertain to the deformation performance, buckling features, and dynamic characteristics, are discussed to verify the advantages of the proposed method.

Convex relaxation for efficient sensor layout optimization in large‐scale structures subjected to moving loads

AbstractThis paper proposes a computationally effective framework for load‐dependent optimal sensor placement in large‐scale civil engineering structures subjected to moving loads. Two common problems are addressed: selection of modes to be monitored and computational effectiveness. Typical sensor placement methods assume that the set of modes to be monitored is known. In practice, determination of such modes of interest is not straightforward. A practical approach is proposed that facilitates the selection of modes in a quasi‐automatic way based on the structural response at the candidate sensor locations to typical operational loads. The criterion used to assess sensor placement is based on Kammer's Effective Independence (EFI). However, in contrast to typical implementations of EFI, which treat the problem as a computationally demanding discrete problem and use greedy optimization, an approach based on convex relaxation is proposed. A notion of sensor density is applied, which converts the original combinatorial problem into a computationally tractable continuous optimization problem. The proposed framework is tested in application to a real tied‐arch railway bridge located in central Poland.

A new optimal light sensor placement method of an indoor lighting control system for improving energy performance and visual comfort

Lighting consumes a substantial amount of energy in buildings. Therefore, the energy can be potentially reduced by implementing optimal control strategies, including an optimal light sensor placement and an intelligent control. This paper presents a new optimal light sensor placement method (OLSPM) of a lighting system in terms of numbers and positions to minimize costs (i.e., initial and energy), and simultaneously maintain the visual comfort that satisfies the standard EN12464-1 in terms of illuminance-based metrics. The optimum position of the sensors was determined based on a proposed mathematical model, which is illuminance center grid, and then, an objective function and its constraints were formulated: average dimming levels of LED luminaires, and illuminance level and uniformity, respectively. Moreover, a new dimming level of luminaires model was proposed to calculate the objective function. Particle swarm optimization (PSO) algorithm was utilized for solving the formulated problem. Finally, the optimum position of the sensors was determined based on the PSO results and the proposed illuminance deviation model. This methodology denoted OLSPM-PSO. The OLSPM-PSO was tested on two case studies: a meeting room and an office room. To evaluate the performance of the OLSPM-PSO with other methods, the OLSPM-PSO was integrated with the developed fuzzy logic controller as a control strategy. The comparative results showed the superiority of the OLSPM-PSO with provided optimum number and position of sensors, significant energy savings up to 24.5% and fully satisfied visual comfort in accordance with EN12464-1 (i.e., average illuminance level and uniformity) and confirmed its potential to be implemented for all types of rooms with higher accuracy.

New Sensor Placement Method Based on Remodeling Innovation

Owing to an increased interest in damage identification and structural health monitoring during dynamic testing, sensor placement optimization has been a challenging research subject over the past decades. As the most influential method developed thus far, the effective independence (EI) method for sensor placement is discussed extensively in the present paper. Specifically, it was observed that, in the computation of the EI index, the sum of each retained row in the eigenvalue contribution matrix is a constant during each iteration. To reveal the significance of this property, a matrix transformation was employed, the form of which is similar to the orthogonal-triangular (QR) decomposition of the EI method previously proposed by the authors. In this regard, the physical significance of the re-orthogonalization of the modes through QR decomposition based on the computation of the EI method can be clearly seen from a new perspective. In view of this, a new sensor placement method, which is referred to as the remodeling innovation (RI) method, is proposed in this paper. The RI method avoids the existing problems in a conventional EI method in that, during each iterative calculation, the row with the least norm is removed without taking into account the effects of the structural mass redistribution. Furthermore, the RI method achieves better sensor placement in terms of the different evaluation criteria applied. Finally, to demonstrate the above observations, the proposed RI method was applied to the Hangzhou Nanshan Bridge.

Optimizing Source and Sensor Placement for Sound Field Control: An Overview

In order to control an acoustic field inside a target region, it is important to choose suitable positions of secondary sources (loudspeakers) and sensors (control points/microphones). This article provides an overview of state-of-the-art source and sensor placement methods in sound field control. Although the placement of both sources and sensors greatly affects control accuracy and filter stability, their joint optimization has not been thoroughly investigated in the acoustics literature. In this context, we reformulate five general source and/or sensor placement methods that can be applied for sound field control. We compare the performance of these methods through extensive numerical simulations in both narrowband and broadband scenarios.

Real-Time Estimation of Wind-Induced Pointing Error via Accelerations Measurement for Large Reflector Antenna

The online monitoring of pointing error (PE) caused by the main reflector (MR) deformation under wind disturbance has become a knotty problem for large reflector antennas. This paper presents a real-time estimation algorithm for monitoring wind-induced PE by optimized acceleration measurement points. First, based on an unbiased minimum-variance input-state estimation theory via accelerations measurement, we set up the PE observation model which illustrates the relation between the structural vibration state and PE. Moreover, compared with the frequency domain integration approach, the non-integral observation model without introducing a low-frequency-cut filter can sense pseudo-static and low-frequency PE. Second, a sensor placement method considering sensor constraint spacing (SPMCSCS) is proposed to achieve a high-quality monitoring effect with low information redundancy. In the method, the constraint spacing between sensors is determined by a novel scan recognition algorithm in which regional division, and regional correlation analysis of the modal shape are carried out successively. Finally, the simulation implementation of a 7.3-m antenna under a wind disturbance is used to illustrate the algorithm.

Optimal Sensor Placement for 2-D Range-Only Target Localization in Constrained Sensor Geometry

Localization of an emitting or reflecting target is one of the most important issues in a wide range of applications including radar, sonar, wireless communication and sensor networks. Due to significant effect on the positioning accuracy, designing the optimal sensor-target geometry has been considered as an important problem in the localization literature. The existing sensor placement methods mainly solve the problem in the cases without any constraints on the sensors locations. In the realistic scenarios, however, the sensors cannot be placed simply in arbitrary locations due to such constraints as the geographical limitations, communication problems between the sensor pairs and the security issues. In this paper, the optimal sensor-target geometries under sensor location constraints for range-based positioning are determined based on the A-optimality, E-optimality and D-optimality criteria. First, the special case of two-sensor in a circular area is considered as the permissible sensors placement region, and then, the results are extended to more number of sensors in a connected arbitrarily-shaped sensor region. The proposed sensor placement method can be applied to any application that the target is located outside the sensor region.

Sensor Placement Optimization Based on an Improved Inertia and Adaptive Particle Swarm Algorithm During an Explosion

An explosion field is random and nonuniform, and test sensors can be damaged by fragments during blast wave monitoring. When accurately and comprehensively obtaining the experimental data and the dynamic distributions from an explosion field, the quantity and positioning of sensors during blast wave monitoring are important parameters. In this paper, an optimization method of sensor placement based on an improved inertia and adaptive particle swarm optimization (IIAPSO) algorithm is proposed to solve this problem. This work considers two new aspects: 1) the adaptive mutation mechanism and the inertia weight into classic particle swarm optimization (PSO) and 2) the propagation law of blast waves, the data errors and the probability of sensor damage during IIAPSO. These mechanisms are employed to enhance the global search ability and to increase data accuracy. First, the 12 benchmark functions are utilized to test the performance of the IIAPSO. The performance of the IIAPSO is compared with PSO, linear decreasing weighted particle swarm optimization (LDW-PSO) and adaptive particle swarm optimization (APSO) in terms of parameter accuracy and convergence speed. The results confirm that the proposed IIAPSO is more successful than PSO, LDW-PSO and APSO algorithms. Finally, the IIAPSO is used to optimize the sensor placement in an explosion field. The simulation and experimental results show that the feasibility of this algorithm is demonstrated.

Monitoring system of high-rise buildings, determined from the nature of the curvature of the elastic line of vertical elements

Introduction. Issues arising from the automatic monitoring of high-rise buildings are considered. One of such issues is the necessity of minimizing the number of sensors when taking the operational patterns of such load-bearing systems. The method of sensor placement in high-rise buildings is described. Materials and methods. Different structural systems of high-rise buildings have common dependencies of longitudinal axis deformation on rigidity of vertical and horizontal load-bearing elements. Calculation of deformations from static wind load, comparison of data obtained by manual and computer calculation, and also comparison of static wind load with the wind load pulsation component are performed. Results. From the analysis of the extrema of the deformation function of the building vertical axis with the horizontal plane of the floor structure preserved, the main typical monitoring points necessary to minimize the number of sensors in high-rise buildings were determined. Based on the typical breaking points, the required number of sensors located 1/4 of the building height away from each other and the range of measured values of deformations that occur during operation were determined. Conclusions. For automatic (constant) monitoring of high-rise buildings, it is advisable to use a complex integrated method of measuring deformations of vertical load-bearing structures, frequencies and amplitudes of oscillations, and rotation angles. According to the general design theory of high-rise buildings and computer calculation models, the most efficient placement of engineering structure monitoring sensors is when they are evenly covering the perimeter of each floor and the entire height of the building. Measurements should be carried out following the outlines of the floor structures on stories located 1/4 of the building height away from each other, as well as on the stiffening core to control the integrity of floor structures and their connection to the stiffness core.

Optimal Sensor Placement Methods in Active High Power Density Electronic Systems With Experimental Validation

Abstract Increasing the efficiency and density of power electronic systems (PESs) is an important objective for many high-impact applications, such as electric vehicle charging and aircraft electrification. Due to compactness and high heat dissipation, careful thermal monitoring of such PESs is required. Strategic placement of temperature sensors can improve the accuracy of real-time temperature distribution estimates. Enhanced temperature estimation supports increased power throughput and density because PESs can be operated in a less conservative manner while still preventing thermal failure. This article presents new methods for temperature sensor placement for 2- and 3-dimensional PESs that (1) improve computational efficiency (by orders of magnitude in at least one case), (2) support the use of more accurate evaluation metrics, and (3) are scalable to high-dimension sensor placement problems. These methods are tested via sensor placement studies based on a single-phase flying capacitor multi-level (FCML) prototype inverter. Information-based metrics are derived from a resistance-capacitance (RC) lumped parameter thermal model. Other more general metrics and system models are possible through the application of a new continuous relaxation strategy introduced here for placement representation. A new linear programming (LP) formulation is presented that is compatible with a particular type of information-based metric. This LP strategy is demonstrated to support an efficient solution of finely discretized large-scale placement problems. The optimal sensor locations obtained from these methods were tested via physical experiments. The new methods and results presented here may aid the development of thermally aware PESs with significantly enhanced capabilities.

Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox.

The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.

Crack Identification of Steel Bar Based on Sensor Optimal Placement

The traditional modal test of steel bars has two chief disadvantages of being time-consuming and having a complex detection operation. This paper presents the effective independence driving-point residue (EI-DPR) method for sensor optimal placement, which identifies cracks of steel bars based on the modal assurance criterion (MAC) and minimum mean square error (MMSE) criterion. The steel bars with cracks are tested separately by the uniform measuring point method and the optimal sensor placement method according to the vibration modal test. By comparing the test results, the optimal sensor placement method can identify cracks rapidly and accurately, which is more efficient than the uniform measuring point method. The study provides a reference for the rapid non-destructive testing of large-aspect ratio steel bars.

Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox

The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.

Interactive planning and sensing in unknown static environments with task-driven sensor placement

We address planar path-planning for a mobile vehicle to traverse a planar workspace with minimum exposure to a spatially varying scalar field called the threat field. The threat field is unknown, and is estimated by a finite number of sensors that take pointwise noisy measurements. We propose an iterative sensor placement and path-planning algorithm. At each iteration, regions of interest in the workspace are identified near the path with minimum estimated cost, and the next set of sensor locations is determined to improve the confidence of threat field estimates in these regions of interest. This paper breaks new ground in dynamic data-driven autonomy, in that an explicit bidirectional interaction between a path-planning algorithm and a sensor network separate from the actor vehicle is established. We provide theoretical results and corroborative numerical simulation results on the convergence and optimality of the proposed iterative algorithm. We show that the proposed algorithm provides significant improvements over existing approaches, including information-maximizing sensor placement methods.

Group Greedy Method for Sensor Placement

This paper discusses greedy methods for sensor placement in linear inverse problems. We comprehensively review the greedy methods in the sense of optimizing the mean squared error (MSE), the volume of the confidence ellipsoid, and the worst-case error variance. We show that the greedy method of optimizing an MSE related cost function can find a near-optimal solution. We then provide a new fast algorithm to optimize the MSE. In greedy methods, we select the sensing location one by one. In this way, the searching space is greatly reduced but many valid solutions are ignored. To further improve the current greedy methods, we propose a group-greedy strategy, which can be applied to optimize all the three criteria. In each step, we reserve a group of suboptimal sensor configurations which are used to generate the potential sensor configurations of the next step and the best one is used to check the terminal condition. Compared with the current greedy methods, the group-greedy strategy increases the searching space but greatly improve the solution performance. We find the necessary and sufficient conditions that the current greedy methods and the proposed group greedy method can obtain the optimal solution. The illustrative examples show that the group greedy method outperforms the corresponding greedy method. We also provide a practical way to find a proper group size with which the proposed group greedy method can find a solution that has almost the same performance as the optimal solution.

Research on the Layout of Real-Time Monitoring Sensors for Long-Span Cable-Stayed Bridges with Twin Towers

Base on a long-span cable-stayed bridge with twin towers. According to the design data and the load test, FEM is established and modified to meet the actual structure. By using the theory of FEA and calculation of the design load and structural mechanics performance, “Theoretical spectra” of static characteristics of structures are established. On the basis of load test, the most unfavorable section and vulnerable parts of the bridge are obtained. Then “Standard spectrum” of bridge structure under the action of standard design load is established. Combining theoretical analysis with the sensor layout method commonly used in long-span cable-stayed bridges, the sensor layout scheme is finally obtained according to the “theoretical spectrum”, the “standard spectrum” and the sensor optimization arrangement theory. The sensor placement method presented in this paper is feasible and effective, and provides a scientific theoretical basis for the sensor placement scheme. It can be used as a real-time monitoring sensor placement method for similar bridges.

Conditional information entropy based sensor placement method considering separated model error and measurement noise

The sensor placement methods for the modal identification are usually based on the prediction error between the measured and the structural model responses. Generally, the prediction errors, which are caused by the model error and the measurement noise, are assumed to be a Gaussian random vector together. This paper proposes a conditional information entropy based sensor placement method to separately investigate the influences of the measurement noise and the model error on the sensor placement. The changes in the element stiffness matrices are used to represent the model error. To quantify the uncertainty in the modal identification result, the conventional information entropy method is not suitable, due to the uncertainty in the Fisher matrix caused by the uncertain model error. This paper proposes a conditional information entropy index to quantify the uncertainty in the modal identification result with uncertain Fisher matrices. The optimal sensor placement corresponds to the one that maximizes the conditional information entropy index value over the set of all possible sensor placements. The conventional information entropy method can be seen as a special case of the proposed conditional information entropy method. In addition, the proposed sensor placement method can be applied to the placement of the multidimensional sensors. The simply supported beam and a bridge benchmark structure are used to explain the sensor placement method, and the redundancy threshold can be set to avoid redundant information provided by closely spaced sensors.

An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

This paper presents a two-stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph-theoretical methods including k-means, k-means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum-inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum-inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.