Charles R. Farrar, Keith Worden

Structural Health Monitoring (eBook, PDF)

A Machine Learning Perspective

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 656
• Erscheinungstermin: 10. Oktober 2012
• Englisch
• ISBN-13: 9781118443200
• Artikelnr.: 37354671

Autorenporträt

Charles R Farrar, Los Alamos National Laboratory, New Mexico, USA is currently the director of The Engineering Institute at LANL. His research interests focus on developing integrated hardware and software solutions to structural health monitoring problems and the development of damage prognosis technology. The results of this research have been documented in 50 refereed journal articles, 14 book chapters, more than 100 conference papers, 31 Los Alamos Reports and numerous keynote lectures at international conferences. In 2000 he founded the Los Alamos Dynamics Summer School. His has recently received the inaugural Los Alamos Fellows Prize for Technical Leadership and the inaugural Lifetime Achievement Award in Structural Health Monitoring. He is currently working with engineering faculty at University of California, San Diego to develop the Los Alamos/UCSD Engineering Institute and Education Initiative with a research focus on Damage Prognosis. He is associate editor for the Int. Journal of Structural Health Monitoring and Earthquake Engineering and Structural Dynamics. Keith Worden, University of Sheffield, UK is Head of the Dynamics Research Group in the Department of Mechanical Engineering at the University of Sheffield. His research interests lie in the applications of advanced signal processing and machine learning methods to structural dynamics. He has authored over 400 research publications including two co-authored books on nonlinear structural dynamics and nonlinear system identification, two book chapters and over 130 refereed journal papers. He serves on the editorial boards of 2 international journals: Journal of Sound and Vibration and Mechanical Systems and Signal Processing. He was awarded "2004 Person of the Year" (jointly with W.J. Staszewski) awarded by Structural Health Monitoring journal for outstanding contribution in the field.

Inhaltsangabe

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