Structural Health Monitoringweb.aeromech.usyd.edu.au/showcase/SMSL-Showcase.pdf · Structural...

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Structural Health Monitoring

Ph.D. Student: Zhongqing SUSupervisor: Dr. Lin Ye

Laboratory of Smart Materials & Structures (LSMS)School of Aerospace, Mechanical & Mechatronic Engineering

University of Sydney, Australia

IntroductionAn Active Online Structural Health Monitoring System Is Developed for the In-Service Composites Using Smart Materials (Piezoelectrics), to Syncretise the Designated Functions With Self-diagnosis and Self-rehabilitation Capabilities, Based on Which the Occurrence and Severity of the Damage Can Be Quantitatively Real-Time monitored.

Digital Damage Fingerprints (DDF)

Damage Parameters Database (DPD)

Artificial Intelligence Algorithm

Advanced Signal/Image Processing Algorithm

Active Sensor/Actuator Network

Active Real-Time Diagnosis System

Highlights

Application FieldsAerospaceAeronauticalMaritimeMechanicalCivil

Delamination

PZT Actuator PZT Actuator

PZT Sensor PZT Sensor

Multi-Lamb

Modes

Delamination

Basic Principle

(Lamb Wave Propagation-Based Identification)

Start

Configuration Design for Built-in Diagnosis Network System

ExperimentalMeasure

Signal Processing via Wavelet Technique

SpectrographicAnalysis

DamageDiagnosis End

Proposed Damage Identification Scheme

Damage ParametersInformation Database

and LibraryDirect Diagnosis

Σ11b

1T11n

Σ12b

1T12n

Σ1jb

1T1jn

.

.

.

Σ21b

2T21n

Σ22b

2T22n

Σ2kb

2T2kn

.

.

....

mim

3im

2im

1im

.

.

Σ31b

3T31n

Σ32b

3T32n

Σ3nb

3T3nn

.

.

....

nov

2ov

1ov

InputLayer

First NeuralProcessing Layer

Output Layer

111−w

1jmw −

211−w

2kjw−

311−w

3nkw−

Characteristicsof Signals

( )ζ

Second NeuralProcessing Layer

Diagnostic Results

ξ( )

( )φ

Artificial Neural Network Technique

– A Simulation of Human Brain

104 105 106 107

0.0

0.2

0.4

0.6

0.8

1.0

Nor

mal

ised

Am

plitu

de

Frequency ( Hz )

0 200 400 600 800 1000

-1.0

-0.5

0.0

0.5

1.0

Nor

mal

ised

Am

plitu

de

Time [ µs ]

5 mm-Depth

FFT

Wavelet Transform

Signal Identification

(Wavelet Algorithm)

Specimen

(Composite Laminate)

Main Control System

Software interface

CCU(HP Vectra Workstation)

Agilent VXI

Agilent ConditionerHP3242

Composite Laminate

P1

Delamination

Piezo System Amplifier EPA-104

SwitchSwitchP1 P3

P2

Fixed Support

Active Online Damage Diagnosis System

(VXI Platform)

Actual Example

Bonded

Piezoelectrics

CCU(HP Vectra Workstation)

Agilent VXI

Agilent ConditionerHP3242

Composite Laminate

P1

Delamination

Piezo System Amplifier EPA-104

SwitchSwitchP1 P3

P2

Fixed Support

Active Diagnosis System

In-Service Aircraft

Identification

Artificial Neural Network

Damage parameters database

Self-Rehabilitation

Failure Diagnosis

Lower Delaminated Surface

Neighboring Area

Upper Delaminated Surface

Case Study (1)

(Delamination Detection in Composites)

Actual Damage

Diagnosis B

Diagnosis C

Diagnosis DDiagnosis E

Case Study (2)

(Hole Detection in Composites)

Diagnosis A/B/C/D/E: Identification using 20/30/40/60/80 damage patterns for sub-database training

Damage Patterns for Network Training

Diagnosis A

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