SELF-SCAN

Neural Net based defect detection system using LRU technology for aircraft structure Monitoring

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 Obiettivo del progetto (Objective)

'This project will develop an integrated system to monitor the condition of aircraft components, using integrated transducer arrays for improved long range ultrasonic testing (LRUT) optimised to maximise UT wave-defect interaction in order to boost sensitivity. The project will: •Improve the defect detection capabilities of guided waves by generating / selecting wavemodes on the basis of optimised wave-defect interaction, rather than selecting one non-dispersive mode facilitating visual signal interpretation, as is the current practise. •Make use of 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, and separate them from changes in signal caused by other factors. •Develop and validate novel flexible MFC transducers / magnetostrictive transducers suitable to be bonded to / integrated into aircraft components to form LRU sensor arrays enabling detection, localisation and sizing of flaws. •Development of Focusing thechniques such as Time reversal focusing and Time delay focusing in complex materials used for aircraft component manufucturing. •Develop, train and validate the Neural Net defect detection and classification system using LRU technology for aircraft components Monitoring. •Develop a central software program with high-level functions comprising data collection, signal processing, data analysis and representation, information storage and user interface. Additional software will be developed to enable focusing of LRU to identifiy significant potential failure sources. •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 components.'

Introduzione (Teaser)

Ultrasound scans are well known for monitoring the health of unborn babies. Now, an EU-funded project has developed a technique combining guided ultrasound wave technology and neural network systems to monitor the health of buried aircraft components.

Descrizione progetto (Article)

Aircraft are large and complex machines, yet even the tiniest crack in the remotest or hardest-to-reach corner can have major consequences in terms of safety and flight worthiness. This makes periodic inspection and maintenance not only vital, but also laborious and time consuming as certain components are concealed beneath layers of other components.

The 'Neural net based defect detection system using LRU technology for aircraft structure monitoring' (http://www.selfscanproject.eu/ (SELF-SCAN)) project developed a technique using guided wave technology to make inspections and maintenance more efficient while enhancing safety. Unlike other approaches to monitoring complex structures, guided wave technology provides large area coverage from a limited number of sensors.

However, aircraft structures as well as the environment in which they interact are complex. Detecting defects from the plethora of geometric data collected using guided ultrasonic waves is therefore an incredibly challenging task. Financed by the EU's Seventh Framework Programme (FP7), SELF-SCAN came up with the novel idea of using neural network systems using permanently installed sensors to enable in situ detection.

With a consortium drawn from six EU Member States, the project team created an advanced integrated system for structural health monitoring and impending failure detection. The prototype system demonstrated its ability to differentiate between sound and defective components, as well as to detect minute but critical cracks in regions considered inaccessible to other sensors.

Once developed further into a commercialised system, ultrasound detection will help bolster safety, lower the risk of catastrophic failure, reduce costs and increase the service life of aircraft components.