Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets
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 m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717743702 |
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| _version_ | 1849410558663589888 |
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| author | Ryan Marks Alastair Clarke Carol A Featherston Rhys Pullin |
| author_facet | Ryan Marks Alastair Clarke Carol A Featherston Rhys Pullin |
| author_sort | Ryan Marks |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7b5f3bbd2c2c4ec09ff6f76fa91c8dc2 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7b5f3bbd2c2c4ec09ff6f76fa91c8dc22025-08-20T03:35:03ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-11-011310.1177/1550147717743702Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data setsRyan MarksAlastair ClarkeCarol A FeatherstonRhys PullinAircraft 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.https://doi.org/10.1177/1550147717743702 |
| spellingShingle | Ryan Marks Alastair Clarke Carol A Featherston Rhys Pullin Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets International Journal of Distributed Sensor Networks |
| title | Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| title_full | Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| title_fullStr | Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| title_full_unstemmed | Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| title_short | Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| title_sort | optimization of acousto ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets |
| url | https://doi.org/10.1177/1550147717743702 |
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