Structural Health Monitoring - A Machine Learning Perspective

Informationen zum Autor 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. Klappentext 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. Zusammenfassung 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 ...

List of contents

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 Feat

Product details

• Authors: Farrar, C. R. Farrar, C. R./ Worden Farrar, Charles Farrar, Charles R Farrar, Charles R. Farrar, Charles R. (Los Alamos National Laboratory Farrar, Charles R. Worden Farrar, Cr Farrar, FARRAR CHARLES R WORDEN KEITH, Farrar Charles R., Keith Worden, Worden Keith
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 08.01.2013
• EAN: 9781119994336
• ISBN: 978-1-119-99433-6
• No. of pages: 680
• Subjects: Natural sciences, medicine, IT, technology > Technology > Structural and environmental engineering; Sensor, Maschinenbau, Tragwerk, Tragwerke, Mechanical Engineering, Bauingenieur- u. Bauwesen, Civil Engineering & Construction, Baustatik u. Baumechanik, Structural Theory & Structural Mechanics, Aeronautic & Aerospace Engineering, Luft- u. Raumfahrttechnik, Structures