Structural Health Monitoring

Preface xvii Acknowledgements xix 1 Introduction 1 1.1 How Engineers and Scientists Study Damage 2 1.2 Motivation for Developing SHM Technology 3 1.3 Definition of Damage 4 1.4 A Statistical Pattern Recognition Paradigm for SHM 7 1.5 Local versus Global Damage Detection 13 1.6 Fundamental Axioms of Structural Health Monitoring 14 1.7 The Approach Taken in This Book 15 References 15 2 Historical Overview 17 2.1 Rotating Machinery Applications 17 2.2 Offshore Oil Platforms 21 2.3 Aerospace Structures 25 2.4 Civil Engineering Infrastructure 32 2.5 Summary 37 References 38 3 Operational Evaluation 45 3.1 Economic and Life-Safety Justifications for Structural Health Monitoring 45 3.2 Defining the Damage to Be Detected 46 3.3 The Operational and Environmental Conditions 47 3.4 Data Acquisition Limitations 47 3.5 Operational Evaluation Example: Bridge Monitoring 48 3.6 Operational Evaluation Example: Wind Turbines 51 3.7 Concluding Comment on Operational Evaluation 52 References 52 4 Sensing and Data Acquisition 53 4.1 Introduction 53 4.2 Sensing and Data Acquisition Strategies for SHM 53 4.3 Conceptual Challenges for Sensing and Data Acquisition Systems 55 4.4 What Types of Data Should Be Acquired? 56 4.5 Current SHM Sensing Systems 60 4.6 Sensor Network Paradigms 63 4.7 Future Sensing Network Paradigms 67 4.8 Defining the Sensor System Properties 68 4.9 Define the Data Sampling Parameters 73 4.10 Define the Data Acquisition System 74 4.11 Active versus Passive Sensing 75 4.12 Multiscale Sensing 75 4.13 Powering the Sensing System 77 4.14 Signal Conditioning 77 4.15 Sensor and Actuator Optimisation 78 4.16 Sensor Fusion 79 4.17 Summary of Sensing and Data Acquisition Issues for Structural Health Monitoring 82 References 83 5 Case Studies 87 5.1 The I-40 Bridge 87 5.2 The Concrete Column 92 5.3 The 8-DOF System 98 5.4 Simulated Building Structure 100 5.5 The Alamosa Canyon Bridge 104 5.6 The Gnat Aircraft 108 References 116 6 Introduction to Probability and Statistics 119 6.1 Introduction 119 6.2 Probability: Basic Definitions 120 6.3 Random Variables and Distributions 122 6.4 Expected Values 125 6.5 The Gaussian Distribution (and Others) 130 6.6 Multivariate Statistics 132 6.7 The Multivariate Gaussian Distribution 133 6.8 Conditional Probability and the Bayes Theorem 134 6.9 Confidence Limits and Cumulative Distribution Functions 137 6.10 Outlier Analysis 140 6.11 Density Estimation 142 6.12 Extreme Value Statistics 148 6.13 Dimension Reduction - Principal Component Analysis 155 6.14 Conclusions 158 References 159 7 Damage-Sensitive Features 161 7.1 Common Waveforms and Spectral Functions Used in the Feature Extraction Process 163 7.2 Basic Signal Statistics 171 7.3 Transient Signals: Temporal Moments 178 7.4 Transient Signals: Decay Measures 181 7.5 Acoustic Emission Features 183 7.6 Features Used with Guided-Wave Approaches to SHM 185 7.7 Features Used with Impedance Measurements 188 7.8 Basic Modal Properties 191 7.9 Features Derived from Basic Modal Properties 206 7.10 Model Updating Approaches 218 7.11 Time Series Models 232 7.12 Feature Selection 234 7.13 Metrics 239 7.14 Concluding Comments 240 References 240 8 Features Based on Deviations from Linear Response 245 8.1 Types of Damage that Can Produce a Nonlinear System Response 245 8.2 Motivation for Exploring Nonlinear System Identification Methods for SHM 248 8.3 Applications of Nonlinear Dynamical Systems Theory 280 8.4 Nonlinear System Identification Approaches 288 8.5 Concluding Comments Regarding Feature Extraction Based on Nonlinear System Response 291 References 292 9 Machine Learning and Statistical Pattern Recognition 295 9.1 Introduction 295 9.2 Intelligent Damage Detection 295 9.3 Data Processing and Fusion for Damage Identification 298 9.4 Statistical Pattern Recognition: Hypothesis Testing 300 9.5 Statistical Pattern Recognition: General Frameworks 303 9.6 Discriminant Functions and Decision Boundaries 306 9.7 Decision Trees 308 9.8 Training - Maximum Likelihood 309 9.9 Nearest Neighbour Classification 312 9.10 Case Study: An Acoustic Emission Experiment 312 9.11 Summary 320 References 320 10 Unsupervised Learning - Novelty Detection 321 10.1 Introduction 321 10.2 A Gaussian-Distributed Normal Condition - Outlier Analysis 322 10.3 A Non-Gaussian Normal Condition - A Neural Network Approach 325 10.4 Nonparametric Density Estimation - A Case Study 329 10.5 Statistical Process Control 338 10.6 Other Control Charts and Multivariate SPC 343 10.7 Thresholds for Novelty Detection 348 10.8 Summary 359 References 359 11 Supervised Learning - Classification and Regression 361 11.1 Introduction 361 11.2 Artificial Neural Networks 361 11.3 A Neural Network Case Study: A Classification Problem 372 11.4 Other Neural Network Structures 374 11.5 Statistical Learning Theory and Kernel Methods 375 11.6 Case Study II: Support Vector Classification 382 11.7 Support Vector Regression 384 11.8 Case Study III: Support Vector Regression 386 11.9 Feature Selection for Classification Using Genetic Algorithms 389 11.10 Discussion and Conclusions 398 References 400 12 Data Normalisation 403 12.1 Introduction 403 12.2 An Example Where Data Normalisation Was Neglected 405 12.3 Sources of Environmental and Operational Variability 406 12.4 Sensor System Design 409 12.5 Modelling Operational and Environmental Variability 411 12.6 Look-Up Tables 414 12.7 Machine Learning Approaches to Data Normalisation 421 12.8 Intelligent Feature Selection: A Projection Method 429 12.9 Cointegration 431 12.10 Summary 436 References 436 13 Fundamental Axioms of Structural Health Monitoring 439 13.1 Introduction 439 13.2 Axiom I. All Materials Have Inherent Flaws or Defects 440 13.3 Axiom II. Damage Assessment Requires a Comparison between Two System States 441 13.4 Axiom III. Identifying the Existence and Location of Damage Can Be Done in an Unsupervised Learning Mode, but Identifying the Type of Damage Present and the Damage Severity Can Generally Only Be Done in a Supervised Learning Mode 444 13.5 Axiom IVa. Sensors Cannot Measure Damage. Feature Extraction through Signal Processing and Statistical Classification Are Necessary to Convert Sensor Data into Damage Information 446 13.6 Axiom IVb. Without Intelligent Feature Extraction, the More Sensitive a Measurement is to Damage, the More Sensitive it is to Changing Operational and Environmental Conditions 447 13.7 Axiom V. The Length and Time Scales Associated with Damage Initiation and Evolution Dictate the Required Properties of the SHM Sensing System 448 13.8 Axiom VI. There is a Trade-off between the Sensitivity to Damage of an Algorithm and Its Noise Rejection Capability 449 13.9 Axiom VII. The Size of Damage that Can Be Detected from Changes in System Dynamics is Inversely Proportional to the Frequency Range of Excitation 451 13.10 Axiom VIII. Damage Increases the Complexity of a Structure 454 13.11 Summary 458 References 459 14 Damage Prognosis 461 14.1 Introduction 461 14.2 Motivation for Damage Prognosis 462 14.3 The Current State of Damage Prognosis 463 14.4 Defining the Damage Prognosis Problem 464 14.5 The Damage Prognosis Process 465 14.6 Emerging Technologies Impacting the Damage Prognosis Process 467 14.7 A Prognosis Case Study: Crack Propagation in a Titanium Plate 468 14.8 Damage Prognosis of UAV Structural Components 474 14.9 Concluding Comments on Damage Prognosis 475 14.10 Cradle-to-Grave System State Awareness 476 References 476 Appendix A Signal Processing for SHM 479 A.1 Deterministic and Random Signals 479 A.2 Fourier Analysis and Spectra 489 A.3 The Fourier Transform 497 A.4 Frequency Response Functions and the Impulse Response 510 A.5 The Discrete Fourier Transform 512 A.6 Practical Matters: Windows and Averaging 525 A.7 Correlations and Spectra 532 A.8 FRF Estimation and Coherence 535 A.9 Wavelets 540 A.10 Filters 564 A.11 System Identification 583 A.12 Summary 591 References 592 Appendix B EssentialLinear StructuralDynamics 593 B.1 Continuous-Time Systems: The Time Domain 593 B.2 Continuous-Time Systems: The Frequency Domain 600 B.3 The Impulse Response 603 B.4 Discrete-Time Models: Time Domain 605 B.5 Multi-Degree-of-Freedom (MDOF) Systems 607 B.6 Modal Analysis 613 References 621 Index 623