• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.1009-1013
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• 2012

A relationship between the sensor placement and the PDOP (Position Dilution of Precision) is derived in the TDOA-based localization system. And the geometric condition of the sensor placement is analyzed in order to get a minimum PDOP based on the derived relationship. Through computer simulations, effect of the sensor placement on the PDOP is observed.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.443-458
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• 2012

The structural health monitoring (SHM) benchmark study on optimal sensor placement problem for the instrumented Canton Tower has been launched. It follows the success of the modal identification and model updating for the Canton Tower in the previous benchmark study, and focuses on the optimal placement of vibration sensors (accelerometers) in the interest of bettering the SHM system. In this paper, the sensor placement problem for the Canton Tower and the benchmark model for this study are first detailed. Then an information entropy based sensor placement method with the purpose of damage detection is proposed and applied to the benchmark problem. The procedure that will be implemented for structural damage detection using the data obtained from the optimal sensor placement strategy is introduced and the information on structural damage is specified. The information entropy based method is applied to measure the uncertainties throughout the damage detection process with the use of the obtained data. Accordingly, a multi-objective optimal problem in terms of sensor placement is formulated. The optimal solution is determined as the one that provides equally most informative data for all objectives, and thus the data obtained is most informative for structural damage detection. To validate the effectiveness of the optimally determined sensor placement, damage detection is performed on different damage scenarios of the benchmark model using the noise-free and noise-corrupted measured information, respectively. The results show that in comparison with the existing in-service sensor deployment on the structure, the optimally determined one is capable of further enhancing the capability of damage detection.

• Smart Structures and Systems
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• v.14 no.1
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• pp.55-70
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• 2014

The paper presents a multi-objective optimization strategy for a multi-type sensor placement for Structural Health Monitoring (SHM) of long span bridges. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on application demands for SHM system. Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE) were chosen as the application demands for SHM. The optimization problem is solved through the use of integer Genetic Algorithm (GA) to maximize a common metric to ensure adequate MI and AMSE. The performance of the joint optimization problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system can improve the quality of SHM. It has also been demonstrated that use of GA improves the overall quality of the sensor placement compared to other methods for optimization of sensor placement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1549-1555
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• 2009

The Relay node placement problem is one of the most important requirements for many wireless sensor networks because the lifetime of sensor networks is closely related with the placement of relay nodes which receive sensed data from sensor nodes and forward them to the base station. Relay node placement problem has focused at minimization of dissipated total energy of the sensor nodes in whole networks. However, minimum total energy causes the unbalance of consumed energy in sensor nodes due to different distances between relay nodes and sensor nodes. This paper proposes the concept of energy balance ratio and finds the locations of relay nodes using objective functions which maximize the energy balance ratio. Maximizing this ratio results in maximizing the network lifetime by minimizing the energy consumption of large-scale sensor networks. However, finding a solution to relay node placement problem is NP-hard and it is very difficult to get exact solutions. Therefore, we get approximate solutions to EBR-RNP problem which considers both energy balance ratio and relay node placement using constraint programming.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.241-247
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• 2011

Although intensive development continues on innovative sensor systems, there is still considerable uncertainty in deciding on the number of sensors required and their locations in order to obtain adequate information on structural behavior. This paper is concerned with the sensor locations on a beam-structure for prognostic structural health monitoring. The purpose of this study is to investigate how to determine optimal sensor placement(OSP) from the sensitivity information of a known failure mode. The sensitivity of the forced vibration response of a beam to the variation of stiffness due to a crack is calculated analytically and used to determine the optimal sensor locations for the specified failure mode. The results of this method compared with the results of different OSP methods. The results have shown that the proposed method on optimal sensor placement is very effective in structural health monitoring.

• Smart Structures and Systems
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• v.13 no.3
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• pp.389-406
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• 2014

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

• Smart Structures and Systems
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• v.4 no.4
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• pp.465-492
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• 2008

Proper pretest planning is a vital component of any successful vibration test on engineering structures. The most important issue in dynamic testing of many engineering structures is arriving at the number and optimal placement of sensors. The sensors must be placed on the structure in such a way that all the important dynamic behaviour of a structural system is captured during the course of the test with sufficient accuracy so that the information can be effectively utilised for structural parameter identification or health monitoring. Several optimal sensor placement (OSP) techniques are proposed in the literature and each of these methods have been evaluated with respect to a specific problem encountered in various engineering disciplines like aerospace, civil, mechanical engineering, etc. In the present work, we propose to perform a detailed characteristic evaluation of some selective popular OSP techniques with respect to their application to practical civil engineering problems. Numerical experiments carried out in the paper on various practical civil engineering structures indicate that effective independence (EFI) method is more consistent when compared to all other sensor placement techniques.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.308-315
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• 1997

In this study, the effect of modal filter error on the vibration control characteristics of flexible structures is analyzed for IMSC(Independent Modal Space Control), and optimal sensor placement in the structural vibration control with consideration of performance of modal filter has been studied. An Lyapunov asymptotic stability condition has been derived, which depends on the magnitude of the modal filter errors. The extent of the response deviation of the closed-loop system is also derived and evaluated using operator techniques. A sensor placement technique has also been suggested to maximize the performance of the modal filter. It has been found by a series of simulation that the suggested sensor placement technique is very effective on the determination of the number and placement of sensors of modal filter in the structural vibration control.