Structural Health Monitoringweb.aeromech.usyd.edu.au/showcase/SMSL-Showcase.pdf · Structural...
Transcript of 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
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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
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Application FieldsAerospaceAeronauticalMaritimeMechanicalCivil
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Delamination
PZT Actuator PZT Actuator
PZT Sensor PZT Sensor
Multi-Lamb
Modes
Delamination
Basic Principle
(Lamb Wave Propagation-Based Identification)
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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
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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
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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)
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Specimen
(Composite Laminate)
Main Control System
Software interface
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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)
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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
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Lower Delaminated Surface
Neighboring Area
Upper Delaminated Surface
Case Study (1)
(Delamination Detection in Composites)
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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