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Identification of Damage Using Lamb Waves - From Fundamentals to Applications
von: Zhongqing Su, Lin Ye
Springer-Verlag, 2009
ISBN: 9781848827844 , 355 Seiten
Format: PDF, OL
Kopierschutz: DRM
Preis: 213,99 EUR
Title Page
2
Preface
6
Contents
8
Introduction
12
Background
12
Lamb-wave-based Damage Identification
14
About this Book
22
References
23
Fundamentals and Analysis of Lamb Waves
26
Retrospect
26
Fundamentals and Theory
26
Theory of Lamb Waves
27
Lamb Waves in Plate of Multiple Layers
33
Shear Horizontal Waves and Love Waves
33
Cylindrical Lamb Waves
35
Propagation Velocity – Phase vs. Group Velocities
36
Slowness
37
Dispersion
38
Numerical and Semi-analytical Study
42
Transfer Matrix Method
44
Global Matrix Method
45
Finite Element Modelling and Simulation
46
Modelling Lamb Waves
46
Modelling Structural Damage
47
Attenuation of Lamb Waves
53
Influence of Temperature
58
Influence of Damage Orientation and Size
59
Summary
62
References
64
Activating and Receiving Lamb Waves
70
Introduction
70
Transducers of Lamb Waves
70
Ultrasonic Probes
70
Piezoelectric Wafers and Piezocomposite Transducers
72
Laser-based Ultrasonics
73
Interdigital Transducers
73
Fibre-optic Sensors – Reception Only
75
Activation of Desired Diagnostic Lamb Waves
75
Selection of Appropriate Wave Mode
75
Optimal Design of Waveform
80
Mechanistic Models of Piezoelectric Transducers
83
Various Models
83
Influence of Transducer Shape
84
Case Study: Activating and Receiving Lamb Waves (Both the S$_{0}$ and A$_{0}$ Modes) in Delaminated Composite Laminates with Surface-bonded PZT Wafers
87
Modelling Coupled PZT Actuator
87
Modelling Coupled PZT Sensor
95
Validation in FEM Simulation
96
Summary
99
References
99
Sensors and Sensor Networks
110
Introduction
110
Piezoelectric Transducer
112
Design of Piezoelectric Actuator and Sensor
113
Surface-mounting vs. Embedding
117
Fibre-optic Sensor
120
Optical Fibre and Fibre-optic Sensor
120
Fibre Bragg Grating Sensor
121
FBG Sensor for Lamb Wave Collection
123
Surface-mounting vs. Embedding
127
Directivity
128
Hybrid Piezoelectric Actuator-optic Sensor Unit
130
Sensor Network
132
Arrangement and Optimisation of Sensor Network
134
Standardised Sensor Network
137
Commercial Sensor Network Techniques
139
Recent Developments
141
Large-scale Sensor Network
141
Wireless Sensor
142
Summary
144
References
145
Processing of Lamb Wave Signals
154
Introduction
154
Digital Signal Processing
155
Time Domain Analysis
155
Frequency Domain Analysis
163
Joint Time-frequency Domain Analysis
166
Wavelet Transform
169
Continuous Wavelet Transform
170
Discrete Wavelet Transform
172
Selection of Wavelet Function
175
Extracting Signal Features Using Wavelet Transform
175
Processing of Lamb Wave Signals
179
Averaging and Normalisation
179
De-noising
181
Feature Extraction and Damage Index
182
Compression
189
A Signal Processing Approach for Lamb Waves: Digital Damage Fingerprints
192
Summary
195
References
197
Algorithms for Damage Identification ? Fusion of Signal Features
205
Introduction
205
Data Fusion and Damage Identification Algorithms
206
Damage Index
209
Time-of-flight
209
Time Reversal – for Identifying Damage
219
Migration Technique
219
Lamb Wave Tomography
223
Probability-based Diagnostic Imaging
229
Phased-array Beamforming
238
Artificial Intelligence
244
Architecture and Scheme of Data Fusion
251
Fusion Architecture
251
Fusion Scheme
253
Summary
256
References
258
Application of Algorithms for Identifying Structural Damage ? Case Studies
265
Identifying a Notch in a Structural Alloy Beam Using Damage Index
265
Establishment of DI
266
Assessing Changes in Notch Size Using DI
268
Locating Delamination in a Composite Panel Using Time-of-flight
271
Hierarchically Locating Multiple Delamination in a Composite Panel Using Time-of-flight
273
Rationale
274
Experimental Validation
275
Evaluating Multiple Delamination in a Composite Panel Using Probability-based Diagnostic Imaging
278
Establishment of Prior Perceptions from a Sensing Path in Terms of ToF
278
Establishment of Probability of Presence of Damage
278
Fusion of Probabilities for Diagnostic Imaging
281
Quantitatively Predicting Delamination in Composite Beams Using an Artificial Neural Network
283
Training Data Preparation
283
ANN Training and Experimental Validation
286
Discussion
286
Quantitatively Estimating Through-thickness Hole and Delamination in Composite Panels Using an Artificial Neural Network
287
Training Data Preparation
287
Parameterised Modelling
290
ANN Training and Experimental Validation
291
Discussion: Influential Issues
293
Identifying Structural Damage in a Composite Laminate Using Bayesian Inference
302
Summary
303
References
303
Systems and Engineering Applications
308
Introduction
308
System for Implementation
308
Signal Generation Subsystem
310
Data Acquisition Subsystem
310
Central Control/Analysis Unit
311
Calibration of System
311
Application in Engineering Structures
314
Detection of Corrosion in Pipelines
314
Identification of Damage in Aerospace Structures
322
Evaluation of Integrity of Civil Infrastructure
329
Summary
333
References
334
Looking Forward
338
State of the Art
338
Prospects
340
References
348
Index
350
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