scholarly journals A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks

2005 ◽  
Vol 9 (1) ◽  
pp. 43-45 ◽  
Author(s):  
F.Y.S. Lin ◽  
P.L. Chiu
Keyword(s):  
Sensor Networks ◽  
Sensor Placement ◽  
Complete Coverage ◽  
Optimal Sensor Placement ◽  
Placement Algorithm

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

scholarly journals Optimal sensor placements using modified Fisher information matrix and effective information algorithm

2021 ◽  
Vol 17 (6) ◽  
pp. 155014772110230
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Fisher Information ◽  
Mode Shape ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Sensor Placement ◽  
Degree Of Freedom ◽  
Optimal Sensor Placement ◽  
Parameter Matrix ◽  
Placement Algorithm ◽  
Sensor Locations

This article presents an optimal sensor placement algorithm for modifying the Fisher information matrix and effective information. The modified Fisher information matrix and effective information are expressed using a dynamic equation constrained by the condensed relationship of the incomplete mode shape matrix. The mode shape matrix row corresponding to the master degree of freedom of the lowest-contribution Fisher information matrix and effective information indices is moved to the slave degree of freedom during each iteration to obtain an updated shape matrix, which is then used in subsequent calculations. The iteration is repeated until the target sensors attain the targeted number of modes. The numerical simulations are then applied to compare the optimal sensor placement results obtained using the number of installed sensors, and the contribution matrices using the Fisher information matrix and effective information approaches are compared based on the proposed parameter matrix. The mode-shape-based optimal sensor placement approach selects the optimal sensor layout at the positions to uniformly allocate the entire degree of freedom. The numerical results reveal that the proposed F-based and effective information–based approaches lead to slightly different results, depending on the number of parameter matrix modes; however, the resulting final optimal sensor placement is included in a group of common candidate sensor locations. However, the resulting final optimal sensor placement is included in a group of common candidate sensor locations.

Bayesian Optimal Sensor Placement for Modal Identification of Civil Infrastructures

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Costas Argyris ◽  
Costas Papadimitriou ◽  
Panagiotis Panetsos
Keyword(s):  
Posterior Distribution ◽  
Sensor Placement ◽  
Modal Identification ◽  
Model Parameters ◽  
Optimal Sensor Placement ◽  
Civil Infrastructures ◽  
Spatially Correlated ◽  
Placement Algorithm ◽  
Insightful Analysis ◽  
Stochastic Optimization Algorithm

A Bayesian optimal experimental design (OED) method is proposed in this work for estimating the best locations of sensors in structures so that the measured data are most informative for estimating reliably the structural modes. The information contained in the data is measured by the Kullback-Leibler (K-L) divergence between the prior and posterior distribution of the model parameters taken in modal identification to be the modal coordinates. The optimal sensor placement that maximizes the expected K-L divergence is shown also to minimize the information entropy of the posterior distribution. Unidentifiability issues observed in existing formulations when the number of sensors is less than the number of identified modes, are resolved using a non-uniform prior in the Bayesian OED. An insightful analysis is presented that demonstrates the effect of the variances of Bayesian priors on the optimal design. For dense mesh finite element models, sensor clustering phenomena are avoided by integrating in the methodology spatially correlated prediction error models. A heuristic forward sequential sensor placement algorithm and a stochastic optimization algorithm are used to solve the optimization problem in the continuous physical domain of variation of the sensor locations. The theoretical developments and algorithms are applied for the optimal sensor placement design along the deck of a 537 m concrete bridge.

Optimal sensor placement for cable force monitoring based on multioutput support vector regression model

2018 ◽  
Vol 21 (15) ◽  
pp. 2259-2269 ◽  
Author(s):  
Shunlong Li ◽  
Huiming Yin ◽  
Zhonglong Li ◽  
Wencheng Xu ◽  
Yao Jin ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Regression Models ◽  
Sensor Placement ◽  
Prediction Performance ◽  
Support Vector ◽  
Optimal Sensor Placement ◽  
Cable Force ◽  
Force Monitoring ◽  
Placement Algorithm ◽  
Cable Forces

Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and the non-monitored (output) cable forces. The number and placement of cable force sensors have significant influence on prediction performance of established multioutput support vector regression models. The proposed optimal sensor configuration is to select multioutput support vector regression models with minimum prediction error from all possible sensor locations. In this study, information entropy is employed to measure the prediction performance of different sensor configurations and formulate the objective function, optimised by three computationally effective algorithms: forward sequential sensor placement algorithm, backward sequential sensor placement algorithm and genetic algorithm. The application of proposed method to Nanjing No. 3 Yangtze River Bridge confirmed the efficiency, accuracy and effectiveness of the proposed method.

A nearly optimal sensor placement algorithm for boundary coverage

2008 ◽  
Vol 41 (11) ◽  
pp. 3343-3355 ◽  
Author(s):  
Andrea Bottino ◽  
Aldo Laurentini
Keyword(s):  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Placement Algorithm
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