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Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical datasets

Marks, Ryan, Clarke, Alastair, Featherston, Carol and Pullin, Rhys 2017. Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical datasets. International Journal of Distributed Sensor Networks 13 (11) 10.1177/1550147717743702

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Abstract

Aircraft structural damage detection is becoming of increased importance. Technologies such as acousto-ultrasonic have been suggested for this application; however, an optimization strategy for sensor network design is required to ensure a high detection probability while minimizing sensor network mass. A methodology for optimizing acousto-ultrasonic transducer placement for adhesive disbond detection on metallic aerospace structures is presented. Experimental data sets were acquired using three-dimensional scanning laser vibrometry enabling in-plane and out-of-plane Lamb wave components to be considered. This approach employs a novel multi-sensor site strategy which is difficult to achieve with physical transducers. Different excitation frequencies and source–damage–sensor paths were considered. A fitness assessment criterion which compared baseline and damaged data sets using cross-correlation coefficients was developed empirically. Efficient sensor network optimization was achieved using a bespoke genetic algorithm for different network sizes with the effectiveness assessed and discussed. A comparable numerical data set was also produced using the local interaction simulation approach and optimized using the same methodology. Comparable results with those of the experimental data set indicated a good agreement. As such, the numerical approach demonstrates that acousto-ultrasonic sensor networks can be optimized using simulation (with some further refinement) during an aircraft design phase, being a useful tool to sensor network designers.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: SAGE Publications
ISSN: 1550-1329
Funders: Engineering and Physical Sciences Research Council
Date of First Compliant Deposit: 1 November 2017
Date of Acceptance: 23 October 2017
Last Modified: 28 Jun 2019 16:39
URI: http://orca.cf.ac.uk/id/eprint/106113

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