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

Ryan Marks, Alastair Clarke, Carol A Featherston and Rhys Pullin

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 11, 1550147717743702

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.

Keywords: Lamb waves; acousto-ultrasonics; optimization; laser vibrometry; local interaction simulation approach; structural health monitoring (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:11:p:1550147717743702

DOI: 10.1177/1550147717743702

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