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Self-Organising Impact Sensing Networks in Robust Aerospace Vehicles

Self-Organising Impact Sensing Networks in Robust Aerospace Vehicles
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Author(s): Mikhail Prokopenko (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Geoff Poulton (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Don Price (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Peter Wang (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Philip Valencia (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Nigel Hoschke (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Tony Farmer (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Mark Hedley (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia), Chris Lewis (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia)and Andrew Scott (CSIRO Information and Communication Technology Centre and CSIRO Industrial Physics, Australia)
Copyright: 2008
Pages: 43
Source title: Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Vijayan Sugumaran (Oakland University, Rochester, USA)
DOI: 10.4018/978-1-59904-941-0.ch057

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Abstract

An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and intelligently in very adverse environments, and be capable of self-monitoring (and ultimately, self-repair). Networks of embedded sensors, active elements, and intelligence have been selected to form a prototypical “smart skin” for the aerospace structure, and a methodology based on multi-agent networks developed for the system to implement aspects of SHM by processes of self-organisation. Problems are broken down with the aid of a “response matrix” into one of three different scenarios: critical, sub-critical, and minor damage. From these scenarios, three components are selected, these being: (a) the formation of “impact boundaries” around damage sites, (b) self-assembling “impact networks”, and (c) shape replication. A genetic algorithm exploiting phase transitions in systems dynamics has been developed to evolve localised algorithms for impact boundary formation, addressing component (a). An ant colony optimisation (ACO) algorithm, extended by way of an adaptive dead reckoning scheme (ADRS) and which incorporates a “pause” heuristic, has been developed to address (b). Both impact boundary formation and ACO-ADRS algorithms have been successfully implemented on a “concept demonstrator”, while shape replication algorithms addressing component (c) have been successfully simulated.

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