Sensor placement optimization in structural health monitoring using genetic and evolutionary algorithms

Huidong Gao; Joseph L. Rose

doi:10.1117/12.657889

11 April 2006 Sensor placement optimization in structural health monitoring using genetic and evolutionary algorithms

Huidong Gao, Joseph L. Rose

Proceedings Volume 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems; 617410 (2006) https://doi.org/10.1117/12.657889
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, California, United States

Abstract

An optimized sensor design and sensor placement strategy will be extremely beneficial to both safety ensuring and cost reduction considerations of structural health monitoring systems. A new framework for structural health monitoring sensor placement optimization was recently developed at Pennsylvania State University based on genetic and evolutionary computation. The formulation of the optimization problem is to minimize the damage misdetection rate as well as to minimize the number of sensors by searching the optimized patterns of sensor placement topology on the feasible region of the structure being monitored. Two types of SHM sensors are considered. One is a single sensor scenario; the other is an actuator-damage-sensor scenario. The program was applied to a sample sensor placement problem of an aging aircraft wing. Optimized sensor placement designs are obtained. The tradeoff relationship between the sensor performance, sensor number, and the overall sensor network performance are also presented in this paper.

Citation Download Citation

Huidong Gao and Joseph L. Rose "Sensor placement optimization in structural health monitoring using genetic and evolutionary algorithms", Proc. SPIE 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 617410 (11 April 2006); https://doi.org/10.1117/12.657889

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