Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgements

Chuck Farrar's Acknowledgements

Keith Worden's Acknowledgements

Chapter 1: Introduction

1.1 How Engineers and Scientists Study Damage

1.2 Motivation for Developing SHM Technology

1.3 Definition of Damage

1.4 A Statistical Pattern Recognition Paradigm for SHM

1.5 Local versus Global Damage Detection

1.6 Fundamental Axioms of Structural Health Monitoring

1.7 The Approach Taken in This Book

References

Chapter 2: Historical Overview

2.1 Rotating Machinery Applications

2.2 Offshore Oil Platforms

2.3 Aerospace Structures

2.4 Civil Engineering Infrastructure

2.5 Summary

References

Chapter 3: Operational Evaluation

3.1 Economic and Life-Safety Justifications for Structural Health Monitoring

3.2 Defining the Damage to Be Detected

3.3 The Operational and Environmental Conditions

3.4 Data Acquisition Limitations

3.5 Operational Evaluation Example: Bridge Monitoring

3.6 Operational Evaluation Example: Wind Turbines

3.7 Concluding Comment on Operational Evaluation

References

Chapter 4: Sensing and Data Acquisition

4.1 Introduction

4.2 Sensing and Data Acquisition Strategies for SHM

4.3 Conceptual Challenges for Sensing and Data Acquisition Systems

4.4 What Types of Data Should Be Acquired?

4.5 Current SHM Sensing Systems

4.6 Sensor Network Paradigms

4.7 Future Sensing Network Paradigms

4.8 Defining the Sensor System Properties

4.9 Define the Data Sampling Parameters

4.10 Define the Data Acquisition System

4.11 Active versus Passive Sensing

4.12 Multiscale Sensing

4.13 Powering the Sensing System

4.14 Signal Conditioning

4.15 Sensor and Actuator Optimisation

4.16 Sensor Fusion

4.17 Summary of Sensing and Data Acquisition Issues for Structural Health Monitoring

References

Chapter 5: Case Studies

5.1 The I-40 Bridge

5.2 The Concrete Column

5.3 The 8-DOF System

5.4 Simulated Building Structure

5.5 The Alamosa Canyon Bridge

5.6 The Gnat Aircraft

References

Chapter 6: Introduction to Probability and Statistics

6.1 Introduction

6.2 Probability: Basic Definitions

6.3 Random Variables and Distributions

6.4 Expected Values

6.5 The Gaussian Distribution (and Others)

6.6 Multivariate Statistics

6.7 The Multivariate Gaussian Distribution

6.8 Conditional Probability and the Bayes Theorem

6.9 Confidence Limits and Cumulative Distribution Functions

6.10 Outlier Analysis

6.11 Density Estimation

6.12 Extreme Value Statistics

6.13 Dimension Reduction – Principal Component Analysis

6.14 Conclusions

References

Chapter 7: Damage-Sensitive Features

7.1 Common Waveforms and Spectral Functions Used in the Feature Extraction Process

7.2 Basic Signal Statistics

7.3 Transient Signals: Temporal Moments

7.4 Transient Signals: Decay Measures

7.5 Acoustic Emission Features

7.6 Features Used with Guided-Wave Approaches to SHM

7.7 Features Used with Impedance Measurements

7.8 Basic Modal Properties

7.9 Features Derived from Basic Modal Properties

7.10 Model Updating Approaches

7.11 Time Series Models

7.12 Feature Selection

7.13 Metrics

7.14 Concluding Comments

References

Chapter 8: Features Based on Deviations from Linear Response

8.1 Types of Damage that Can Produce a Nonlinear System Response

8.2 Motivation for Exploring Nonlinear System Identification Methods for SHM

8.3 Applications of Nonlinear Dynamical Systems Theory

8.4 Nonlinear System Identification Approaches

8.5 Concluding Comments Regarding Feature Extraction Based on Nonlinear System Response

References

Chapter 9: Machine Learning and Statistical Pattern Recognition

9.1 Introduction

9.2 Intelligent Damage Detection

9.3 Data Processing and Fusion for Damage Identification

9.4 Statistical Pattern Recognition: Hypothesis Testing

9.5 Statistical Pattern Recognition: General Frameworks

9.6 Discriminant Functions and Decision Boundaries

9.7 Decision Trees

9.8 Training – Maximum Likelihood

9.9 Nearest Neighbour Classification

9.10 Case Study: An Acoustic Emission Experiment

9.11 Summary

References

Chapter 10: Unsupervised Learning – Novelty Detection

10.1 Introduction

10.2 A Gaussian-Distributed Normal Condition – Outlier Analysis

10.3 A Non-Gaussian Normal Condition – A Neural Network Approach

10.4 Nonparametric Density Estimation – A Case Study

10.5 Statistical Process Control

10.6 Other Control Charts and Multivariate SPC

10.7 Thresholds for Novelty Detection

10.8 Summary

References

Chapter 11: Supervised Learning – Classification and Regression

11.1 Introduction

11.2 Artificial Neural Networks

11.3 A Neural Network Case Study: A Classification Problem

11.4 Other Neural Network Structures

11.5 Statistical Learning Theory and Kernel Methods

11.6 Case Study II: Support Vector Classification

11.7 Support Vector Regression

11.8 Case Study III: Support Vector Regression

11.9 Feature Selection for Classification Using Genetic Algorithms

11.10 Discussion and Conclusions

References

Chapter 12: Data Normalisation

12.1 Introduction

12.2 An Example Where Data Normalisation Was Neglected

12.3 Sources of Environmental and Operational Variability

12.4 Sensor System Design

12.5 Modelling Operational and Environmental Variability

12.6 Look-Up Tables

12.7 Machine Learning Approaches to Data Normalisation

12.8 Intelligent Feature Selection: A Projection Method

12.9 Cointegration

12.10 Summary

References

Chapter 13: Fundamental Axioms of Structural Health Monitoring

13.1 Introduction

13.2 Axiom I. All Materials Have Inherent Flaws or Defects

13.3 Axiom II. Damage Assessment Requires a Comparison between Two System States

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

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

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

13.7 Axiom V. The Length and Time Scales Associated with Damage Initiation and Evolution Dictate the Required Properties of the SHM Sensing System

13.8 Axiom VI. There is a Trade-off between the Sensitivity to Damage of an Algorithm and Its Noise Rejection Capability

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

13.10 Axiom VIII. Damage Increases the Complexity of a Structure

13.11 Summary

References

Chapter 14: Damage Prognosis

14.1 Introduction

14.2 Motivation for Damage Prognosis

14.3 The Current State of Damage Prognosis

14.4 Defining the Damage Prognosis Problem

14.5 The Damage Prognosis Process

14.6 Emerging Technologies Impacting the Damage Prognosis Process

14.7 A Prognosis Case Study: Crack Propagation in a Titanium Plate

14.8 Damage Prognosis of UAV Structural Components

14.9 Concluding Comments on Damage Prognosis

14.10 Cradle-to-Grave System State Awareness

References

Appendix A: Signal Processing for SHM

A.1 Deterministic and Random Signals

A.2 Fourier Analysis and Spectra

A.3 The Fourier Transform

A.4 Frequency Response Functions and the Impulse Response

A.5 The Discrete Fourier Transform

A.6 Practical Matters: Windows and Averaging

A.7 Correlations and Spectra

A.8 FRF Estimation and Coherence

A.9 Wavelets

A.10 Filters

A.11 System Identification

A.11 Summary

References

Appendix B: Essential Linear Structural Dynamics

B.1 Continuous-Time Systems: The Time Domain

B.2 Continuous-Time Systems: The Frequency Domain

B.3 The Impulse Response

B.4 Discrete-Time Models: Time Domain

B.5 Multi-Degree-of-Freedom (MDOF) Systems

B.6 Modal Analysis

References

Index

This edition first published 2013
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Library of Congress Cataloging-in-Publication Data

Farrar, C. R. (Charles R.)
Structural health monitoring : a machine learning perspective / Charles R. Farrar, Keith Worden.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-119-99433-6 (cloth)
1. Structural health monitoring. I. Worden, K. II. Title.
TA656.6.F37 2012
624.1′71-dc23

2012018036

A catalogue record for this book is available from the British Library.

ISBN: 978-1-119-99433-6

CRF: To Lorraine, I can't imagine receiving more support and encouragement!

KW: To Anna and George with love.

Preface

This book is the result of the two authors' collaborations on the subject of Structural Health Monitoring (SHM) dating back to the mid-1990s. It is interesting to point out that the two authors, somewhat independently and at approximately the same time, decided that the statistical pattern recognition approach to SHM was the best framework to use for these problems. With this realisation, it was then possible to tap into the extensive set of pattern recognition algorithms developed by the statistics and machine learning communities and effectively apply them to the damage detection problem even though in almost all cases they were not originally developed with damage detection in mind. This viewpoint on SHM has developed to a major extent within the literature and it is therefore a primary goal for this book to provide the readers with a summary of the structured statistical pattern recognition approach and introduce them to the tools of machine learning as they are applied in the context of the SHM problem. In addition, the authors hope that the readers will appreciate the very general nature of this approach and see that it is well suited to deal with all the sources of variability encountered in any real-world damage detection problem.

The authors believe that the material is presented at a level suitable for upper-level undergraduates, postgraduate students and practising engineers. In fact, much of the material in this book is based on industry short courses that have been taught more than 20 times since 1997, a graduate class on SHM that has been taught numerous times at the University of California, San Diego and a graduate class taught since 2002 at the University of Sheffield. An attempt has been made to produce a book that is as self-contained as possible and the two appendices on signal processing and linear structural dynamics were added with that goal in mind. As such, readers at the levels listed above with an appropriate numerate background should find that they do not need too many prerequisites to understand the majority of the material presented in this book. However, readers should keep in mind that SHM is a very multidisciplinary topic and the materials presented herein would ‘traditionally’ be covered to some degree in courses on structural dynamics, nondestructive evaluation, signal processing, detection theory, machine learning, probability and statistics, and sensor networks. When trying to cover such a broad range of technologies, the authors here were faced with the formidable challenge of striking a balance between a complete and detailed treatment of these subjects while maintaining the book at a reasonable length. This has led to difficult decisions regarding the content; clearly, many chapters could themselves be expanded into books (e.g. Chapter 4, Sensing and Data Acquisition Issues). The tack taken here is to explain the material in sufficient detail that the reader can understand the concepts and key issues. The authors then make use of citations to point the reader to more detailed summaries of the various topics. Finally, it should be pointed out that the material is presented serially, following the statistical pattern recognition paradigm introduced in the first chapter, but the authors recommend that this statistical pattern recognition paradigm should actually be thought of and implemented in a much more integrated manner.

In addition to presenting the theory for the various aspects of the SHM process, the authors have tried to demonstrate the concepts in a variety of manners. These demonstrations include numerical simulations, tests on well-controlled laboratory experiments specifically designed for SHM and, when possible, a wide variety of examples on ‘real-world’ structures ranging from civil infrastructure to telescopes to aircraft. These demonstrations are not only supposed to show how a particular method works but they are also intended to highlight issues and challenges with these various methods.

When one states that they are going to take a statistical pattern recognition approach to the SHM problem and make use of machine learning algorithms to implement this approach, there is often the misconception that this precludes the use of physical models. The development of such physical models has traditionally been the mainstay of engineering research. However, because of the widely varying length and time scales associated with damage initiation and evolution, the geometric complexity of most real-world systems and the inevitable operational and environmental variability encountered in most SHM problems, such physical modelling for SHM becomes challenging. It should be made clear that the statistical pattern recognition approach, which is the foundation for this book, in no way precludes the use of such physical models. If such models are available, and have been validated, the SHM process can only be improved based on insights gained from these models. However, by taking a pattern recognition approach, we are not constraining ourselves to a particular model form and, in a sense, we are allowing the structure to ‘talk to us’ more directly.

Finally, it must be acknowledged that for most applications SHM is still primarily a research topic. The authors believe this book gives an up-to-date summary of the field and provides the most general approach to addressing the SHM problem that has been proposed to date. However, it is anticipated that this technology will continue to evolve with new methodologies continually being proposed. Even for the topics presented herein, the discussions are by no means complete. As an example, in Chapter 7 there are many more features that have been proposed in the literature that are not summarised in this chapter. Therefore, the readers are encouraged to seek out the other books that have been published on SHM, most of which have appeared in the last ten years, for a broader treatment of this subject. Because of the life-safety and economic advantages that SHM solutions can provide, the future is very bright for this technology. The applications of SHM are expanding and diversifying. As such, the authors hope this book contributes to the ‘health’ of this technical field.

Acknowledgements

The work summarised in this book represents contributions from numerous students and colleagues who both authors have been interacting with over the last 20 years. An attempt is made to identify the many people whose work is in some way highlighted in this book. The difficulty with such acknowledgements is that inevitably someone is inadvertently left out and the authors would like to apologise for such omissions up front. These acknowledgements are spelled out chapter by chapter for each author

Chuck Farrar's Acknowledgements

A great deal of thanks must go to the UK Royal Academy of Engineering for the award of a Distinguished Visitor Fellowship for this author; that fellowship was used to complete large portions of this book. Next, Mike Todd from the University of California, San Diego (UCSD) and Gyuhae Park from Los Alamos National Laboratory (LANL) are gratefully acknowledged. They have helped develop, evolve and run the LANL-UCSD Engineering Institute for the last 10 years where it has grown from a dynamics summer school into an international education and research collaboration. Through their endeavors, this institute has been able to maintain a focus on SHM research and keep this research thrust active and growing throughout this time. Furthermore, this author has enjoyed the support of all his LANL managers over the years he has pursued SHM research. In particular, he would like to acknowledge the support and friendship of Steve Girrens, who is currently the Associate Director for Engineering at LANL. This author has worked with Steve for 29 years and his backing and encouragement have been extremely valuable from so many different perspectives.

Chapter 1. For this author the initial ideas for the statistical pattern recognition paradigm, which is the foundation of this book, came primarily from discussions in the mid-1990s with two computer scientists at LANL, Vance Faber and Dave Nix, who used a similar approach to address many of their pattern classification problems.

Chapter 2. Much of the material for this overview has come from two extensive literature reviews that were the effort of many contributing authors. The lead authors on these reviews were Scott Doebling, when he was a postdoctoral fellow at LANL, and Hoon Sohn, when he was a technical staff member at LANL. Tom Duffey is also acknowledged for contributing to the portion of this chapter related to rotating machinery.

Chapter 4. The wireless sensor node work highlighted in this chapter has been the work of a group of graduate research assistants at LANL starting with Neal Tanner and followed by David Allen, Jarrod Dove, David Mascarenas, Tim Overly, and currently led by Stuart Taylor. The helicopter wireless energy delivery system was the focus of David Mascarenas' PhD dissertation. LANL technical staff members Gyuhae Park and Kevin Farinholt have been the primary mentors for these students. Gyuhae Park has also led the impedance-based sensing work briefly summarised in this chapter.

Chapter 5. Many people from New Mexico State University (NMSU), Sandia National Laboratory (SNL) and LANL took part in the I-40 Bridge test. First, Ken White from NMSU must be acknowledged as the tests were his idea and he secured the funding for this project. Albert Migliori is also acknowledged for inviting this author to participate in this project, which is how he got started in the field of SHM. Two students, Kerry Cone from the University of New Mexico (UNM) and Wayne McCabe from NMSU, along with Bill Baker from UNM and Randy Mayes from SNL were key to the success of this project.

 Gerard Pardoen from the University of California–Irvine must be thanked for inviting this author to participate in his concrete column testing program. This work was carried out with the help of Phil Cornwell from the Rose-Hulman Institute of Technology and Erik Strasser and Hoon Sohn, who at that time were graduate students at Stanford University.

  Bill Baker from UNM is again acknowledged for his work in designing the eight-degree-of-freedom test structure. Similarly, David Mascarenas designed the simulated building structure and was responsible for its fabrication while he was a graduate research assistant at LANL.

  As with the I-40 Bride tests, numerous people have participated in the many tests performed on the Alamosa Canyon Bridge since 1996. Again, Ken White at NMSU is credited with getting this bridge designated as a test structure by the New Mexico Highway and Transportation Department. Scott Doebling and Phil Cornwell led many of the tests whose results are summarized in this book.

Chapter 6. Brett Nadler and Jeni Wait, who were technical staff members at LANL, are thanked for their impact testing of the composite plates and subsequent ultrasonic scanning of these plates.

Chapter 7. Francois Hemez from LANL is gratefully acknowledged for his help with the section on temporal moments and guidance on the model updating sections of this chapter. Scott Doebling was also a significant contributor to the model updating sections. Gyuhae Park and Eric Flynn from LANL have provided considerable input to the guided wave and impedance measurements sections. Kevin Farinholt is thanked for his development of the test structure shown in Section 7.2 and Chris Stull from LANL is thanked for the numerical simulations summarised in Section 7.3. The COMAC values for the I-40 Bridge were computed by Eloi Figuereido as part of his graduate studies carried out at LANL. The load-dependent Ritz vectors are taken from work done by Hoon Sohn when he was a graduate student at Stanford University. Results from the I-40 Bridge shown throughout this chapter are taken from work done by David Jauregui from NMSU when he was a graduate research assistant at LANL. Dave Nix was the person who introduced this author to the use of time series models for damage detection. The mutual information results are taken from the work that Tim Edwards at SNL did as part of a project for this author's SHM class.

Chapter 8. Amy Robertson is acknowledged for her work on the Holder exponent that she performed when she was a technical staff member at LANL.

Chapter 10. The control charts shown in this chapter were generated by Eloi Figuereido as part of his graduate studies carried out at LANL. Chris Stull, Jim Wren and Stuart Taylor from LANL are acknowledged for their experimental and analytical work on the Raptor telescope project.

Chapter 11. The example on support vector regression is based on the work performed by Luke Bornn from the University of British Columbia when he was a LANL graduate research assistant.

Chapter 12. This author would like to thank Gyuhae Park for impedance measurement material presented in the sensor system design section. Mike Todd is acknowledged for providing the data from the composite-hull ship and Hoon Sohn is thanked for his extensive analysis of these data. Again, Eloi Figuereido must be thanked for the comparative study of the various machine learning algorithms that was part of his graduate studies carried out at LANL. Dustin Harvey did much of the work to define the look-up table example while he was a graduate research assistant at LANL.

Chapter 13. Once more, Gyuhae Park is thanked for the impedance measurement example used in this chapter.

Chapter 14. This chapter is taken extensively from an article that appeared in the Philosophical Transactions of the Royal Society and this author wants to thank the co-author of that article, Nick Lieven, currently a pro-vice chancellor at the University of Bristol, for allowing us to use this material in this book.

Keith Worden's Acknowledgements

In Sheffield, the main acknowledgements are to Wieslaw Staszewski (now in AGH University Krakow) and Graeme Manson, who have been working in the field of SHM with the author since he started and have been an unfailing source of ideas and support throughout. Keith would also like to thank Geof Tomlinson for providing the post-doctoral position that distracted him into SHM from the (then) comfort zone of nonlinear system identification. In the research climate prevalent now, it is almost impossible to make progress without the support of talented and dedicated PhD students and Research Associates; Keith considers himself blessed to have had the opportunity to collaborate with a stream of outstanding researchers (in very rough chronological order): Janice Dulieu-Barton, Cecilia Surace, Julian Chance, Andreas Kyprianou, Hoon Sohn, Karen Holford, Rhys Pullin, Mark Eaton, David Allman, Gaetan Kerschen, Tze Ling Lew, Daley Chetwynd, Gareth Pierce, Faizal Mustapha, Jose Zapico, Luis Mujica, Mike Todd, Gyuhae Park, Frank Stolze, Jyrki Kullaa, Arnaud Deraemaeker, Evangelos (Vaggelis) Papatheou, Rob Barthorpe, Thariq Hameed bin Sultan, Anees Ur Rehman and Lizzy Cross. Many of these people are still valued colleagues, although some inevitably escaped. Keith would also like to thank a stream of excellent and committed undergraduate and masters project students who carried out work to such a high standard that it was published. Many of the results in the current book were at least influenced by the work of such students and in some cases their actual results are included; if a direct attribution is absent in any case, this is an omission and is subject to apology. On a chapter-by-chapter basis, Keith would like to thank a number of people whom Chuck hasn't already acknowledged.

Chapter 9. The acoustic emission work was carried out using experimental data provided by Professor Karen Holford and Dr Rhys Pullin of the University of Wales Cardiff – some of the detailed analysis is the work of Steve Rippengill as part of his MEng project work. (AE data provided by Karen and Rhys also appears in the brief illustration of PCA in Chapter 6.)

Chapter 10. The results in Section 10.3 pertaining to operational variations are the result of joint work with Cecilia Surace of the Politecnico di Torino, Italy. It was through work with Cecilia that the importance of operational and environmental variations first impinged on Keith. The work on the Gnat aircraft presented in Chapter 10, like all work presented throughout on the Gnat, only happened because of financial support from the late Dr David Allman of DERA (now QinetiQ) and the collaboration (and saint-like patience) of Dr Graeme Manson (Sheffield). By supporting our SHM work, David allowed a much more thorough access to experimental validation than would have been possible for us otherwise; as a friend and colleague he is sadly missed. The section on control charts in Chapter 10 owes much to material Keith has learned from Jyrki Kullaa (Aalto University, Helsinki, Finland). Finally, the material on extreme value statistics owes a great deal to numerous discussions over the years with Professor Hoon Sohn of KAIST, South Korea.

Chapter 11. Again, Keith would like to thank Graeme Manson for his collaboration on the Gnat aircraft. The analysis of the Gnat data based on support vector machines was largely carried out by Alex Lane as part of his undergraduate project. In terms of genetic optimisation for feature seclection, Keith thanks Vaggelis Papatheou and Graeme Manson again; much of the detailed analysis in the final section was carried out as part of the final year project of Gabrielle Hilson.

Chapter 12. The material on cointegration in this chapter was provided by Dr Lizzy Cross, to whom sincere thanks are due.

Appendix B. The material on the composite beam experiment was provided by Mr Nikolaos (Nikos) Dervilis (Sheffield) and is appreciated.

Throughout, any data presented on Lamb wave propagation as part of a Sheffield collaboration almost always arose from work carried out with Dr Gareth Pierce and Professor Brian Culshaw of the University of Strathclyde, Glasgow, Scotland, or with Professor Wieslaw Staszewski (now AGH University, Krakow, Poland). Also, Keith would like to thank Dr Rob Barthorpe (Sheffield) for a number of interesting and enlightening conversations on SHM over recent years, particularly pertaining to model-based approaches and issues of validation and verification.

Finally, Keith would like to apologise to his children Anna and George for all the time spent writing this book (among other things) that was not spent with them. Thanks for your patience and understanding guys, I'll try and be better.

Overall from both authors, the foremost acknowledgement here must go to Dr Lizzy Cross for her careful proofreading of, and comments on, almost all of the manuscript. Her efforts ensured that (sometimes rough) drafts were converted into chapters and that the minimum of mistakes survived into the final versions. It should be said that any mistakes that do remain are the fault of the authors and if any readers become aware of such mistakes the authors would like to be informed (but politely ). Finally, the authors would like to thank a number of the Wiley editors, latterly Ms Liz Wingett, for their polite but firm attempts to stop them missing an infinite sequence of deadlines.

1

Introduction

Modern societies are heavily dependent upon structural and mechanical systems such as aircraft, bridges, power generation systems, rotating machinery, offshore oil platforms, buildings and defence systems. Many of these existing systems are currently nearing the end of their original design life. Because these systems cannot be economically replaced, techniques for damage detection are being developed and implemented so that these systems can continue to be safely used if or when their operation is extended beyond the design basis service life. Also, in terms of the design and introduction of new engineering systems, these often incorporate novel materials whose long-term degradation processes are not well understood. In the effort to develop more cost-effective designs, these new systems may be built with lower safety margins. These circumstances demand that the onset of damage in new systems can be detected at the earliest possible time in an effort to prevent failures that can have grave life-safety and economic consequences.

Damage detection is usually carried out in the context of one or more closely related disciplines that include: structural health monitoring (SHM), condition monitoring (CM), nondestructive evaluation (NDE) – also commonly called nondestructive testing, or (NDT), health and usage monitoring system (HUMS), statistical process control (SPC) and damage prognosis (DP).

The term structural health monitoring (SHM) usually refers to the process of implementing a damage detection strategy for aerospace, civil or mechanical engineering infrastructure. This process involves the observation of a structure or mechanical system over time using periodically spaced dynamic response measurements, the extraction of damage-sensitive features from these measurements and the statistical analysis of these features to determine the current state of system health. For long-term SHM, the output of this process is periodically updated information regarding the ability of the structure to continue to perform its intended function in light of the inevitable ageing and degradation resulting from the operational environments. Under an extreme event, such as an earthquake or unanticipated blast loading, SHM could be used for rapid condition screening, to provide, in near real time, reliable information about the performance of the system during the event and about the subsequent integrity of the system.

Condition monitoring is analogous to SHM, but specifically addresses damage detection in rotating and reciprocating machinery, such as that used in manufacturing and power generation (Worden and Dulieu-Barton, 2004).

Both SHM and CM have the potential to be applied on-line, that is during operation of the system or structure of interest. In contrast, nondestructive evaluation (NDE) is usually carried out off-line after the site of the potential damage has been located. There are exceptions to this rule, as NDE is also used as a monitoring tool for in situ structures such as pressure vessels and rails. NDE is therefore primarily used for damage characterisation and as a severity check when there is a priori knowledge of the damage location (Shull, 2002).

Health and usage monitoring systems (HUMSs) are closely related to CM systems, but the term has largely been adopted for the specific application to damage detection in rotorcraft drive trains (Samual and Pines, 2005). In that context, the health monitoring portion of the process attempts to identify damage, while the usage monitoring records the number of load cycles that the system experiences for the purposes of calculating fatigue life consumption.

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