Identification of Damage Using Lamb Waves - From Fundamentals to Applications

Identification of Damage Using Lamb Waves - From Fundamentals to Applications

von: Zhongqing Su, Lin Ye

Springer-Verlag, 2009

ISBN: 9781848827844 , 355 Seiten

2. Auflage

Format: PDF, OL

Kopierschutz: Wasserzeichen

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Identification of Damage Using Lamb Waves - From Fundamentals to Applications


 

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