selfscan€¦ · lrut transducer selection & fatigue experiment 4. evaluation of the use of...

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Copyright © TWI Ltd 2012 SelfScan Neural Net based defect detection system using Long Range Ultrasonic Testing (LRUT) technology for Aircraft Structure Health Monitoring 1 February 2010 - 30 April 2012 FP7: Research for the benefit of SMEs Project Leader: Kamer Tuncbilek LRU Section -TWI Cambridge, UK 19/04/2012 www.selfscanproject.eu/

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Page 1: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

SelfScan Neural Net based defect detection system using Long

Range Ultrasonic Testing (LRUT) technology for

Aircraft Structure Health Monitoring

1 February 2010 - 30 April 2012

FP7: Research for the benefit of SMEs

Project Leader: Kamer Tuncbilek LRU Section -TWI

Cambridge, UK

19/04/2012

www.selfscanproject.eu/

Page 2: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Agenda

13.00 hours

1. Welcome and Introduction: How the SelfScan journey started

2. The application scenarios and expected benefits due to the use of Long-Range Ultrasound Techniques (LRUT) in maintenance operations

3. LRUT transducer selection & fatigue experiment

4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect presence

4. Open discussions and feedback

16.00 hours Meeting Close

www.selfscanproject.eu/

Page 3: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

About myself..

• Education

– BSc in Industrial Engineering

– MA in Business Administration

– MPhil in Industrial Systems, Manufacture and

Management / University of Cambridge

• At TWI

– Senior Project Leader at LRU - Managing several EU

funded R4S projects

www.selfscanproject.eu/

Page 4: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

About TWI

• Membership-Based

• Non profit distributing

• 60 years track record

• Around 800 staff

• £60M turnover

• International & UK regional centers

• Specialised in

– Materials joining, coating,

processing

– Performance & life

management

– NDT

www.selfscanproject.eu/

Page 5: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

About SelfScan

• Neural Net based defect detection system using Long Range Ultrasonic Testing (LRUT) technology for Aircraft Structure Health Monitoring

• Partly funded by the FP7 programme (Research for the benefit of SMEs) over two years

• 7 partners (2 RTDs, 4 SMEs, 1 LE) from 6 countries across the Europe

• Project manager: Kamer Tuncbilek - TWI Ltd

www.selfscanproject.eu/

Page 6: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

SelfScan Project Consortium

SMEs

RTD Performers

End Users

Optel (Poland) TWI (UK)

Cereteh (Greece)

NDT Expert

(France)

Phillips

Consultants

(UK)

Isotest

Engineering s.r.l

(Italy)

Smart Material

GmbH

(Germany)

www.selfscanproject.eu/

Page 7: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Project Overview How? Why? What?

www.selfscanproject.eu/

Page 8: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Market need

Fact 1: Air travel is

arguably the safest

mode of travel

Fact 2: But when

something goes wrong,

it goes horribly wrong

Fact 3: Failure due to

undetected defects is

still a big problem for the

aerospace industry

Source: New scientist

www.selfscanproject.eu/

How

?

Wh

y?

Wh

at?

Page 9: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Market need

• Need for the development of NDT technologies for

monitoring aircraft structures

• Present technologies are limited by their coverage

• Need for distributed sensors to improve the

coverage area as well as continuous monitoring

• An aircraft structure deals with high stresses

– Wind

– Temperature

– Pressure etc.

www.selfscanproject.eu/

How

?

Wh

y?

Wh

at?

Page 10: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Causes of Air Crashes

www.selfscanproject.eu/

22%

Pilot Error

 Other Human Error

 Weather

 Mechanical Failure

 Sabotage

 Other Cause

PlaneCrashInfo.com database

How

?

Wh

y?

Wh

at?

Page 11: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

To Improve Air Travel Reliability..

• Set up a structural safety system

Design

Manufacture

Maintenance

Inspection

Repair

Health Monitoring

Damage Detection

Structural

safety

www.selfscanproject.eu/

How

?

Wh

y?

Wh

at?

Page 12: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

SHM..

Structural health monitoring (SHM) needs to:

• Allow continuous monitoring

• Reduce the time for inspections

• Reduce the time for repeated dismantling

• Reduce the costs associated with those

two above

www.selfscanproject.eu/

How

?

Wh

y?

Wh

at?

Page 13: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

• Continuous health monitoring and non-

destructive assessment of aircraft

structures

Long Range Ultrasonics + Neural Networks

•Structure monitoring

•Defect detection

•Signal analysis

SelfScan

www.selfscanproject.eu/

How

?

Wh

y?

Wh

at?

Page 14: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

Project Technical Objectives

• Develop advanced NDT

technique – Long Range Ultrasonics (LRUT)

• Advanced signal processing – Neural Networks

• Final trials – Laboratory experiments on

representative structures

www.selfscanproject.eu/

Sound propagating in one of the

critical components in an aircraft

Transducer

location

Complex

reflections

Wh

y?

Wh

at?

H

ow

?

Page 15: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

SelfScan system offer:

• Novel structural health monitoring system

especially for inaccessible areas

• Rapid screening of structure for in-service defect

detection

• High defect detection sensitivity

• Reduction in costs of gaining access to

inaccessible areas for inspection

• Full coverage of airframe structures with minimum

number of transducers

www.selfscanproject.eu/

Wh

y?

Wh

at?

H

ow

?

Page 16: SelfScan€¦ · LRUT transducer selection & fatigue experiment 4. Evaluation of the use of neural network systems to classify complex signals for the automatic diagnosis of defect

Copyright © TWI Ltd 2012

For more information please visit

www.selfscanproject.eu/

or contact

Kamer Tuncbilek on [email protected]

Thank you for your attention