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