Robust Sensor Placement Problem in Municipal Water Networks

2010 ◽  
Author(s):  
Xin Ma ◽  
Yuantao Song ◽  
Jun Huang ◽  
Jun Wu
Keyword(s):  
Sensor Placement ◽  
Water Networks ◽  
Placement Problem ◽  
Municipal Water

scholarly journals Sensor Placement in Municipal Water Networks

2003 ◽  
Author(s):  
Jonathan Berry ◽  
Lisa Fleischer ◽  
William Hart ◽  
Cynthia Phillips
Keyword(s):  
Sensor Placement ◽  
Water Networks ◽  
Municipal Water

scholarly journals Sensor Placement in Municipal Water Networks

2005 ◽  
Vol 131 (3) ◽  
pp. 237-243 ◽  
Author(s):  
Jonathan W. Berry ◽  
Lisa Fleischer ◽  
William E. Hart ◽  
Cynthia A. Phillips ◽  
Jean-Paul Watson
Keyword(s):  
Sensor Placement ◽  
Water Networks ◽  
Municipal Water

Sensor Placement in Municipal Water Networks with Temporal Integer Programming Models

2006 ◽  
Vol 132 (4) ◽  
pp. 218-224 ◽  
Author(s):  
Jonathan Berry ◽  
William E. Hart ◽  
Cynthia A. Phillips ◽  
James G. Uber ◽  
Jean-Paul Watson
Keyword(s):  
Integer Programming ◽  
Sensor Placement ◽  
Programming Models ◽  
Water Networks ◽  
Municipal Water

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

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

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.

scholarly journals Sensor Placement via Optimal Experiment Design in EMI Sensing of Metallic Objects

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Lin-Ping Song ◽  
Leonard R. Pasion ◽  
Nicolas Lhomme ◽  
Douglas W. Oldenburg
Keyword(s):  
Experimental Design ◽  
Field Experiments ◽  
Information Gain ◽  
Estimation Error ◽  
Sensor Placement ◽  
Model Parameters ◽  
Placement Problem ◽  
Empirical Survey ◽  
Optimal Experiment ◽  
Linearized Model

This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI) sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED) technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

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