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On 19th April 2012 the SelfScan Project will be holding a half day Webcast. more info..

This project will develop an integrated system to monitor continuously the condition of aircraft components, using a novel integrated transducers for improved long range ultrasonic technique (LRUT) optimised to maximise UT wave-defect interaction in order to boost sensitivity The project will:

  • Boost the defect detection capabilities of guided waves by generating / selecting wavemodes on the basis optimised wave-defect interaction, rather than selecting one non-dispersive mode facilitating visual signal interpretation, as is the current practise.
  • Utilise Neural Nets for data interpretation and defect classification. Neural Nets are, in a monitoring type system, ideally suited to detect minute changes in signals, caused by defect initiation and subsequent growth.
  • Develop and validate novel flexible MFC transducers / magnetostrictive transducers suitable to be bonded to / integrated aircraft components with LRU capability enabling detection, localisation and sizing of flaws.
  • Develop and validate the first Neural Net defect detection system using LRU technology for aircraft components Monitoring. This advanced signal analysis sytem will improve the propability of defect detection and enhance the signal to noise ratio.
  • Undertake modular integrations of the sensors/transducers, signal processing and software functionalities to develop the prototypes and demonstrate its the capability to monitor, to reduce the maintenance costs and increase the safety of aircraft component.