damage identification in beam-like structures by vibration...

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Dr. M. ŞAHİN

AE 713 - Experimental Analysis of Vibrating Structures

Damage Identification in Beam-like Structures

by Vibration-based Analysis and

Artificial Neural Networks

Dr. M. ŞAHİN

Dr. M. ŞAHİN

OUTLINE

• Background• Research Problem

– Structures• Composite Beam• Steel Beam• Sandwich Beam

– Real-time Monitoring and Sensor Network• Accelerometers• Strain Gauges• Fibre Optic Strain Sensors

– Damage Detection Algorithm• Artificial Neural Networks

• Analysis Results• Conclusions

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Dr. M. ŞAHİN

BACKGROUND

SMART Structure?Sensor Actuator Controller Processor

Smart Structure • • • •

Adaptive Structure • • •

Active Structure • •

Sensory Structure •

Passive Structure

Substructure

Disturbance

Sensor

Actuator

Controller Feedback Voltage

Sensor Output

Actuating Signal

Processor

Network

Dr. M. ŞAHİN

BACKGROUND

Other Techniques: Non Destructive Inspection

• Requiring direct access

• Taking the structure out of service for inspection

• Routine

• Passive

• Requiring large human involvement

• Costly

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Dr. M. ŞAHİN

BACKGROUND

• Designing an on-line structural health monitoring system

• Need for an intelligent algorithm– Non-unique, non-linear and inverse problem

– Pattern recognition capabilities

• Developments in vibration sensors

Dr. M. ŞAHİN

BACKGROUND

Advantages of using SMART Materials

• Reduction in weight

• Reduction in cost

• Robustness

• Improvement on the system performance

• Predictable and fast response

• Reliability

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Dr. M. ŞAHİN

Structural Health Monitoring System

• Is there a damage in the system?

• Where is the damage in the system?

• What kind of damage?

• How severe is the damage?

• How much useful life remains?

EXISTENCE

LOCATION

TYPE

EXTENT

PREDICTION

BACKGROUND

Structural Health Monitoring System

• Is there a damage in the system?

• Where is the damage in the system?

• What kind of damage?

• How severe is the damage?

• How much useful life remains?

Dr. M. ŞAHİN

BACKGROUND

Bonding?

Location? How many?� Optimum

placement� Optimum

number of sensors

Located close to the area of damage

� Surface mounted

� Embedded

Less susceptibility to accidental movement or damageUsed when free surfaces are not available

Investigation of Sensors

SENSORS

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Dr. M. ŞAHİN

BACKGROUND• Damage!!

– The structural change that adversely affects the current and future performance of the structure and leads a change in its dynamic response

• Damage Detection!! – To express the use of simulated or measured

structural responses in detecting changes in the condition of the structure

Dr. M. ŞAHİN

RESEARCH PROBLEM

Self LearningSmart Structure for

DAMAGE DETECTION

Structure Sensor NetworkReal-time Sensing

Algorithm


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Dr. M. ŞAHİN

METHOD

• Model-Based Structural Analysis – Finite Element Modelling and Analysis

• Intact Structure

• Modelling of Damage

• Damage Scenarios

• Dynamic Analysis

– Experimental Analysis• Specimen manufacturing

• Sensory Structure: Integration of Sensors

• Vibration experiments

• Data processing

Dr. M. ŞAHİN

Damage Types in Composite Structures

• Delaminating

• Crack Growth

• Fibre Breakage

• Impact Damage

Damage Models in FEA

• Reduction in stiffness

• Reduction in thickness

• Impulse loading

Local damage model

Impact damage model

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RESEARCH PROBLEM


DAMAGE DETECTION


Algorithm


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STRUCTURE– Beam-like Structures

• Submarines

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• Offshore Platforms

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• Sailing Boats

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• Aircrafts and Helicopters

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• Aircraft Carries

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RESEARCH PROBLEM


DAMAGE DETECTION


Algorithm


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REAL-TIME MONITORING

• Advantages of real-time monitoring– On-line load monitoring

– Information on service life

– Design verification

– Damage detection and characterisation

– Improved inspection and maintenance schedules

– Improved safety and performance

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Dr. M. ŞAHİN

• Fibre optic strain sensors– Fibre Bragg gratings

• Advantages– Chemical and physical compatibility

• Embedding applications

– Light in weight

– Multiple sensors on a single fibre

– Simultaneous interrogation of multiple sensors

– Immunity to electromagnetic interference

– Withstand harsh environment

SENSOR NETWORK

Dr. M. ŞAHİN

SENSOR NETWORK

(A)

(B)

• (A) Sandwich Beam with Embedded Fibre Bragg Gratings • (B) Steel Beam with Electrical Resistance Strain Gages

+ + + + + ++

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RESEARCH PROBLEM


DAMAGE DETECTION

StructureCompositeSandwich

Sensor NetworkReal-time Sensing

Algorithm


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DAMAGE DETECTION ALGORITHM

• Artificial Neural Networks• An information-processing algorithm

• Non-linear parameterised mapping

– Capabilities• Applicability to problems that do not have an

algorithmic solution

• Generalisation (Incomplete data)

• Robustness in noise-polluted data

• Suitability for real-time applications

“Multi-Layer Feed-Forward Back-Propagation”

- Pattern Associator -

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Predictions

Damage Sensitive Signal Features from the Sensory Structure

Summary:

Dr. M. ŞAHİN

APPROACH to the PROBLEM

• Finite Element Modelling and Analysis– Beam-like Structure

– Modal Analysis

• Sensitivity Analyses – Percentage Reduction in Natural Frequencies

– Absolute Differences in Curvature Mode Shapes

• Neural Networks– Damage Severity Predictions

– Damage Location Predictions

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Dr. M. ŞAHİN

STRUCTURE – Numerical Study

Cantilever Composite Beam– Geometrical Properties(Normalised):

– Material Properties(Dimensionless):

– Layer Orientation:Four-layer, equal thickness, symmetric, cross-ply,

Dr. M. ŞAHİN

STRUCTURE- Beam Finite Element Model

Finite Element Model of the Cantilever Composite Beam(Top view)

0.55Lw

L

Damaged Area

Collocation Point

Damage Location

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Dr. M. ŞAHİN

FINITE ELEMENT MODELLING

Dr. M. ŞAHİN

SENSITIVITY ANALYSES: Part I

Percentage Reduction in Natural Frequencies

Damage located at 0.20L Damage located at 0.35L

Damage Severity (% Reduction in Stiffness)

Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency


Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency

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Dr. M. ŞAHİN




Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency


Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency


Dr. M. ŞAHİN




Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency


Per

cent

age

Red

uct

ion

in N

atur

al F

requ

ency


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SENSITIVITY ANALYSES: Part II

Absolute Differences in Curvature Mode Shapes

along the beam

Damage located at 0.20LMode No: 2


Mag

nitu

de

Measurement Locations [L]

Damage Severity

Mag

nitu

de


Damage Severity

Dr. M. ŞAHİN

SENSITIVITY ANALYSES: Part II

Absolute Differences in Curvature Mode Shapes

along the beam



Mag

nitu

de


Damage Severity

Mag

nitu

de


Damage Severity

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Pre-Processing

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- Pattern Associator -• Multi-Layer

– Input, output and hidden layers

• Feed-Forward Back-Propagation– Training iterations from input layer to output– To adjust the weights so that introducing of set of

inputs produces the desired set of outputs– Error values are calculated and fed in the backward

direction to minimise the error function

“Multi-Layer Feed-Forward Back-Propagation”

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ARTIFICIAL NEURAL NETWORK

Input Data

• RNF : Reduction in (Normalised) Natural Frequency

• MADC : Maximum Absolute Differences in

Curvature Mode Shape

• LOC : Location where MADC occurs

Output Data

• DS :Severity of the Damage

• DL :Location of the Damage

• DS&DL : Both Severity and Location of the Damage

Architecture

a:b:cNumber of Input Number of Output

Number of Neuronsin the Hidden Layer

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ARTIFICIAL NEURAL NETWORK

+

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RESULTS: ANN Predictions- Noise Free Data

RNF for

Damage Severity

RNF for

Damage Location

Tra

inin

g

Val

idat

ion

Number of Epochs

Mean Square Error

Number of Epochs Target

Target

Pre

dict

ed V

alu

esP

redi

cted

Val

ues

Tra

inin

g

Val

idat

ion

Dr. M. ŞAHİN


MADCfor

Damage Location

MADC&LOC for

Damage Location

Target

Target

Pre

dict

ed V

alu

esP

redi

cted

Val

ues

Tra

inin

g

Val

idat

ion

Tra

inin

g

Val

idat

ion

Number of Epochs

Number of Epochs

Mean Square Error

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RNF&MADC&LOCfor

Damage Severity and Location

Damage LocationDamage SeverityNumber of Epochs

Mean Square Error

Tra

inin

g

Val

idat

ion

Target Target

Pre

dict

ed V

alu

es

Pre

dict

ed V

alu

es

Dr. M. ŞAHİN

RESULTS: ANN Predictions- Data with Noise

RNF for

Damage Severity

0.5% Noise on RNF

1% Noise on RNF 2% Noise on RNF

Pre

dict

ed V

alu

es

Pre

dict

ed V

alu

es

TargetTarget

Pre

dict

ed V

alu

es

Target

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Dr. M. ŞAHİN


MADC&LOC for

Damage Location

1% Noise on MADC

5% Noise on MADC3% Noise on MADC

Pre

dict

ed V

alu

es

Pre

dict

ed V

alu

es

Target

Target Target

Pre

dict

ed V

alu

es

Dr. M. ŞAHİN


Damage Severity

0.5% Noise on RNF and 1% Noise on MADC

Damage Location

RNF&MADC&LOCfor


Target Target

Pre

dict

ed V

alu

es

Pre

dict

ed V

alu

es

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Dr. M. ŞAHİN


RNF&MADC&LOCfor


Pre

dict

ed V

alu

es

Target

Damage Severity

Target

Pre

dict

ed V

alu

es

Damage Location

1% Noise on RNF and 3% Noise on MADC

Dr. M. ŞAHİN


RNF&MADC&LOCfor


Pre

dict

ed V

alu

es

Target

Damage Severity

Target

Pre

dict

ed V

alu

es

Damage Location

2% Noise on RNF and 5% Noise on MADC

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Dr. M. ŞAHİN

CONCLUSIONS -Composite Beam

• Completely numerical study has been performed.

• Vibration-based feature extraction coupled with a trained ANN provides the basis for real time damage assessment.

Dr. M. ŞAHİN

CONCLUSIONS -Composite Beam

• Although reduction in natural frequencies is considered as an indicator for the existence of the damage and its severity, they did not provide any useful information about the location of the damage.

• Maximum absolute differences in curvature mode shapes and their corresponding locations along the beam served as better indicators for the location of the damage. Therefore, these features were used as separate input for the ANNs.

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Dr. M. ŞAHİN

STRUCTURE

Cantilever Steel Beam

Dr. M. ŞAHİN


Beam elements

Shell elements

Solid elements

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Dr. M. ŞAHİN

STRUCTURE- Specimen

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EXPERIMENTAL SET-UP

PC

PC

PC

Digital Vibration Controller& Data Acquisition Unit

Power Amplifier

Electro-dynamic Vibration Generator

Clamp

Beam

Signal Conditioning

Amplifier

Data Acquisition Unit

Fibre Optic Strain Measurement System

Accelerometer(Control)

StrainGages

Fibre Optic Sensors

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EXPERIMENTAL SET-UP

Strain gauge conditioning unit

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EXPERIMENTAL SET-UP

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STRUCTURE – Experimental Study

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Strain values for intact steel beam for different frequency ranges1-20 Hz

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Strain values for damagedsteel beam for different frequency ranges1-20 Hz

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Normalised absolute curvature mode shapes of intact steel beamMode 1

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Normalised absolute curvature mode shapes of damagedsteel beamMode 1

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Experimental Analysis for Strain Mode Shapes

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1501000

Intact Beam – Mode 1 Damaged Beam – Mode 1

Dr. M. ŞAHİN


Normalised experimental strain mode shapes of intact beam(a) Mode 1 (b) Mode 2 (c) Mode 3

Normalised experimental strain mode shapes of damaged beam(a) Mode 1 (b) Mode 2 (c) Mode 3

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CONCLUSIONS -Steel Beam

• Experimental varification has been performed.

• Different damage scenarios have been created by reducing the local thickness of the selected elements at different locations.

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Dr. M. ŞAHİN

CONCLUSIONS -Steel Beam

• It can be concluded from the ANN predictions that the better accuracy has been achieved in severity predictions than the location ones in noise-free case.

• Introducing an artificial noise on noise-free data has adversely affected the severity predictions although the results are still accurate for the location predictions obtained from each ANN used in the verification by using an experimental data.

Dr. M. ŞAHİN

STRUCTURE

Cantilever Sandwich Beam• Core: Linear polymer foam (Core-Cell® A500)

• Matrix: Prime 20 epoxy infusion system

• Fibre Reinforcement: Uni-directional (UD) glass reinforcing fibre (UT-E500)

are used during the manufacturing of sandwich beam specimens.

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STRUCTURE

Cantilever Sandwich Beam450mm x 40.5mm x 14.6mm beam

Total thickness of 1.3mm on each side of 12mm thick foam core.

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Fabrication of the Sandwich Beam Specimens with Embedded FOSs

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• A Teflon tape is inserted between the foam core and the GFRP skin along the length of the sandwich beam.

• The locations are measured from the fixed end to the centre of the damage.

Introducing of Damage

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• The first three resonant frequencies of the intact and four damaged beams are obtained under random excitation in

the range of 10Hz to 710Hz.

Frequency Measurements

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450mm x 40.5mm x 14.6mm beam

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Effect of Boundary Condition

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Sensitivity Analysis on Changes in Frequency

• Damage scenarios are extended by interpolating the normalised natural frequencies for the damage extents between 10mm and 50mm by an increment of 1mm which gives 41 different damage extents at 26 different damage locations along the beam.

• The normalised natural frequencies from 1066 damage cases are obtained from the first three vertical bending modes.

Dr. M. ŞAHİN


Variation of normalised natural frequencies: Mode 1

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STRUCTURE – Numerical StudyPredictions

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STRUCTURE – Numerical StudyPredictions – Input: RNF Output: DS&DL

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CONCLUSIONS -Sandwich Beam

• More accurate estimation in localisation of the damage compared to quantification.

• Fibre optic Bragg grating sensors are powerful tool for real-time structural health monitoring.

• Technology provides improved safety, performance and service life for GFRP composite structures.

damage identification in beam-like structures by vibration...

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