Ultrasonic sensor placement optimization in structural health monitoring using evolutionary strategy

H. Gao, J. L. Rose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

In structural health monitoring (SHM), sensor network scale and sensor distribution decisions are critical since sensor network performance and system cost are greatly affected. A quantitative sensor placement optimization method with covariance matrix adaptation evolutionary strategy (CMAES) is presented in this paper. A damage detection probability model is developed for ultrasonic guided wave sensor networks. Two sample problems are presented in this paper. One is for structure with irregular damage distribution probability, and the other is for an E2 aircraft wing section. The reliability of this genetic and evolutionary optimization method is proved in this study. Sensor network configurations with minimum missed-detection probability are obtained from the results of evolutionary optimization. The tradeoff relationship between optimized sensor network performance and the number of sensors is also presented in this paper.

Original languageEnglish (US)
Title of host publicationReview of Progress in Quantitative Nondestructive Evaluation
Subtitle of host publicationVolume 25B
Pages1687-1693
Number of pages7
DOIs
StatePublished - Mar 6 2006
EventReview of Progress in Quantitative Nondestructive - Brunswick, ME, United States
Duration: Jul 31 2005Aug 5 2005

Publication series

NameAIP Conference Proceedings
Volume820 II
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

OtherReview of Progress in Quantitative Nondestructive
CountryUnited States
CityBrunswick, ME
Period7/31/058/5/05

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All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

Gao, H., & Rose, J. L. (2006). Ultrasonic sensor placement optimization in structural health monitoring using evolutionary strategy. In Review of Progress in Quantitative Nondestructive Evaluation: Volume 25B (pp. 1687-1693). (AIP Conference Proceedings; Vol. 820 II). https://doi.org/10.1063/1.2184724