Structural Health Monitoring
A Machine Learning Perspective
1. Auflage November 2012
654 Seiten, Hardcover
Wiley & Sons Ltd
Kurzbeschreibung
The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). In the first comprehensive book on the general problem of structural health monitoring, the authors, renowned experts in the field, consider structural health monitoring in the context of a machine learning/statistical pattern recognition paradigm. They first explain the paradigm in general terms and then shed light on the process in detail with further insights from numerical and experimental studies of laboratory test specimens and in-situ structures. A must for researchers, practicing engineers, and university faculty working in the SHM field.
Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM.
Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.
Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors' detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.
* Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
* Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
* Benefits from extensive use of the authors' detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
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
