Identification of Damage Using Lamb Waves (eBook)

The authors are active researchers in the areas of smart materials and structures. In particular, Prof Ye is an internationally renowned research leader in advanced materials. His major research interests are in the general areas of composites science and technology, intelligent structures, nano-composites, structural integrity and durability. The research team, headed by Prof. Ye, has contributed fruitful achievements to damage identification using active sensor networks. Lin Ye has over 300 scientific publications including 180 refereed papers in leading international journals.

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