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|a MAIN
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|a Farrar, C. R.
|q (Charles R.)
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|a Structural health monitoring
|h [electronic resource] :
|b a machine learning perspective /
|c Charles R. Farrar, Keith Worden.
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| 264 |
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1 |
|a Chichester, West Sussex, U.K. ;
|a Hoboken, N.J. :
|b Wiley,
|c 2012.
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a Includes bibliographical references and index.
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|a Print version record and CIP data provided by publisher.
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|a 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 -- 1.4.1. Operational Evaluation -- 1.4.2. Data Acquisition -- 1.4.3. Data Normalisation -- 1.4.4. Data Cleansing -- 1.4.5. Data Compression -- 1.4.6. Data Fusion -- 1.4.7. Feature Extraction -- 1.4.8. Statistical Modelling for Feature Discrimination -- 1.5. Local versus Global Damage Detection -- 1.6. Fundamental Axioms of Structural Health Monitoring -- 1.7. The Approach Taken in This Book -- References -- 2. Historical Overview -- 2.1. Rotating Machinery Applications -- 2.1.1. Operational Evaluation for Rotating Machinery -- 2.1.2. Data Acquisition for Rotating Machinery -- 2.1.3. Feature Extraction for Rotating Machinery -- 2.1.4. Statistical Modelling for Damage Detection in Rotating Machinery.
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|a 2.1.5. Concluding Comments about Condition Monitoring of Rotating Machinery -- 2.2. Offshore Oil Platforms -- 2.2.1. Operational Evaluation for Offshore Platforms -- 2.2.2. Data Acquisition for Offshore Platforms -- 2.2.3. Feature Extraction for Offshore Platforms -- 2.2.4. Statistical Modelling for Offshore Platforms -- 2.2.5. Lessons Learned from Offshore Oil Platform Structural Health Monitoring Studies -- 2.3. Aerospace Structures -- 2.3.1. Operational Evaluation for Aerospace Structures -- 2.3.2. Data Acquisition for Aerospace Structures -- 2.3.3. Feature Extraction and Statistical Modelling for Aerospace Structures -- 2.3.4. Statistical Models Used for Aerospace SHM Applications -- 2.3.5. Concluding Comments about Aerospace SHM Applications -- 2.4. Civil Engineering Infrastructure -- 2.4.1. Operational Evaluation for Bridge Structures -- 2.4.2. Data Acquisition for Bridge Structures -- 2.4.3. Features Based on Modal Properties.
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|a 2.4.4. Statistical Classification of Features for Civil Engineering Infrastructure -- 2.4.5. Applications to Bridge Structures -- 2.5. Summary -- References -- 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 -- 4. Sensing and Data Acquisition -- 4.1. Introduction -- 4.2. Sensing and Data Acquisition Strategies for SHM -- 4.2.1. Strategy I -- 4.2.2. Strategy II -- 4.3. Conceptual Challenges for Sensing and Data Acquisition Systems -- 4.4. What Types of Data Should Be Acquired? -- 4.4.1. Dynamic Input and Response Quantities -- 4.4.2. Other Damage-Sensitive Physical Quantities -- 4.4.3. Environmental Quantities.
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|a 4.4.4. Operational Quantities -- 4.5. Current SHM Sensing Systems -- 4.5.1. Wired Systems -- 4.5.2. Wireless Systems -- 4.6. Sensor Network Paradigms -- 4.6.1. Sensor Arrays Directly Connected to Central Processing Hardware -- 4.6.2. Decentralised Processing with Hopping Connection -- 4.6.3. Decentralised Processing with Hybrid Connection -- 4.7. Future Sensing Network Paradigms -- 4.8. Defining the Sensor System Properties -- 4.8.1. Required Sensitivity and Range -- 4.8.2. Required Bandwidth and Frequency Resolution -- 4.8.3. Sensor Number and Locations -- 4.8.4. Sensor Calibration, Stability and Reliability -- 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.
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|a 4.17. Summary of Sensing and Data Acquisition Issues for Structural Health Monitoring -- References -- 5. Case Studies -- 5.1. The I-40 Bridge -- 5.1.1. Preliminary Testing and Data Acquisition -- 5.1.2. Undamaged Ambient Vibration Tests -- 5.1.3. Forced Vibration Tests -- 5.2. The Concrete Column -- 5.2.1. Quasi-Static Loading -- 5.2.2. Dynamic Excitation -- 5.2.3. Data Acquisition -- 5.3. The 8-DOF System -- 5.3.1. Physical Parameters -- 5.3.2. Data Acquisition -- 5.4. Simulated Building Structure -- 5.4.1. Experimental Procedure and Data Acquisition -- 5.4.2. Measured Data -- 5.5. The Alamosa Canyon Bridge -- 5.5.1. Experimental Procedures and Data Acquisition -- 5.5.2. Environmental Measurements -- 5.5.3. Vibration Tests Performed to Study Variability of Modal Properties -- 5.6. The Gnat Aircraft -- 5.6.1. Simulating Damage with a Modified Inspection Panel -- 5.6.2. Simulating Damage by Panel Removal -- References -- 6. Introduction to Probability and Statistics.
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|a 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 -- 6.10.1. Outliers in Univariate Data -- 6.10.2. Outliers in Multivariate Data -- 6.10.3. Calculation of Critical Values of Discordancy or Thresholds -- 6.11. Density Estimation -- 6.12. Extreme Value Statistics -- 6.12.1. Introduction -- 6.12.2. Basic Theory -- 6.12.3. Determination of Limit Distributions -- 6.13. Dimension Reduction -- Principal Component Analysis -- 6.13.1. Simple Projection -- 6.13.2. Principal Component Analysis (PCA) -- 6.14. Conclusions -- References -- 7. Damage-Sensitive Features.
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|a 7.1.Common Waveforms and Spectral Functions Used in the Feature Extraction Process -- 7.1.1. Waveform Comparisons -- 7.1.2. Autocorrelation and Cross-Correlation Functions -- 7.1.3. The Power Spectral and Cross-Spectral Density Functions -- 7.1.4. The Impulse Response Function and the Frequency Response Function -- 7.1.5. The Coherence Function -- 7.1.6. Some Remarks Regarding Waveforms and Spectra -- 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 -- 7.6.1. Preprocessing -- 7.6.2. Baseline Comparisons -- 7.6.3. Damage Localisation -- 7.7. Features Used with Impedance Measurements -- 7.8. Basic Modal Properties -- 7.8.1. Resonance Frequencies -- 7.8.2. Inverse versus Forward Modelling Approaches to Feature Extraction -- 7.8.3. Resonance Frequencies: The Forward Approach.
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|a 7.8.4. Resonance Frequencies: Sensitivity Issues -- 7.8.5. Mode Shapes -- 7.8.6. Load-Dependent Ritz Vectors -- 7.9. Features Derived from Basic Modal Properties -- 7.9.1. Mode Shape Curvature -- 7.9.2. Modal Strain Energy -- 7.9.3. Modal Flexibility -- 7.10. Model Updating Approaches -- 7.10.1. Objective Functions and Constraints -- 7.10.2. Direct Solution for the Modal Force Error -- 7.10.3. Optimal Matrix Update Methods -- 7.10.4. Sensitivity-Based Update Methods -- 7.10.5. Eigenstructure Assignment Method -- 7.10.6. Hybrid Matrix Update Methods -- 7.10.7. Concluding Comment on Model Updating Approaches -- 7.11. Time Series Models -- 7.12. Feature Selection -- 7.12.1. Sensitivity Analysis -- 7.12.2. Information Content -- 7.12.3. Assessment of Robustness -- 7.12.4. Optimisation Procedures -- 7.13. Metrics -- 7.14. Concluding Comments -- References -- 8. Features Based on Deviations from Linear Response -- 8.1. Types of Damage that Can Produce a Nonlinear System Response.
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|a 8.2. Motivation for Exploring Nonlinear System Identification Methods for SHM -- 8.2.1. Coherence Function -- 8.2.2. Linearity and Reciprocity Checks -- 8.2.3. Harmonic Distortion -- 8.2.4. Frequency Response Function Distortions -- 8.2.5. Probability Density Function -- 8.2.6. Correlation Tests -- 8.2.7. The Holder Exponent -- 8.2.8. Linear Time Series Prediction Errors -- 8.2.9. Nonlinear Time Series Models -- 8.2.10. Hilbert Transform -- 8.2.11. Nonlinear Acoustics Methods -- 8.3. Applications of Nonlinear Dynamical Systems Theory -- 8.3.1. Modelling a Cracked Beam as a Bilinear System -- 8.3.2. Chaotic Interrogation of a Damaged Beam -- 8.3.3. Local Attractor Variance -- 8.3.4. Detection of Damage Using the Local Attractor Variance -- 8.4. Nonlinear System Identification Approaches -- 8.4.1. Restoring Force Surface Model -- 8.5. Concluding Comments Regarding Feature Extraction Based on Nonlinear System Response -- References.
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|a 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 -- 9.10.1. Analysis and Classification of the AE Data -- 9.11. Summary -- References -- 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 -- 10.4.1. The Experimental Structure and Data Capture -- 10.4.2. Preprocessing of Data and Features -- 10.4.3. Novelty Detection.
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|a 10.5. Statistical Process Control -- 10.5.1. Feature Extraction Based on Autoregressive Modelling -- 10.5.2. The X-Bar Control Chart: An Experimental Case Study -- 10.6. Other Control Charts and Multivariate SPC -- 10.6.1. The S Control Chart -- 10.6.2. The CUSUM Chart -- 10.6.3. The EWMA Chart -- 10.6.4. The Hotelling or Shewhart T2 Chart -- 10.6.5. The Multivariate CUSUM Chart -- 10.6.6. The Multivariate EWMA Chart -- 10.7. Thresholds for Novelty Detection -- 10.7.1. Extreme Value Statistics -- 10.7.2. Type I and Type II Errors: The ROC Curve -- 10.8. Summary -- References -- 11. Supervised Learning -- Classification and Regression -- 11.1. Introduction -- 11.2. Artificial Neural Networks -- 11.2.1. Biological Motivation -- 11.2.2. The Parallel Processing Paradigm -- 11.2.3. The Artificial Neuron -- 11.2.4. The Perceptron -- 11.2.5. The Multilayer Perceptron -- 11.3.A Neural Network Case Study: A Classification Problem -- 11.4. Other Neural Network Structures.
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|a 11.4.1. Feedforward Networks -- 11.4.2. Recurrent Networks -- 11.4.3. Cellular Networks -- 11.5. Statistical Learning Theory and Kernel Methods -- 11.5.1. Structural Risk Minimisation -- 11.5.2. Support Vector Machines -- 11.5.3. Kernels -- 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 -- 11.9.1. Feature Selection Using Engineering Judgement -- 11.9.2. Genetic Feature Selection -- 11.9.3. Issues of Network Generalisation -- 11.9.4. Discussion and Conclusions -- 11.10. Discussion and Conclusions -- References -- 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.
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|a 12.7. Machine Learning Approaches to Data Normalisation -- 12.7.1. Auto-Associative Neural Networks -- 12.7.2. Factor Analysis -- 12.7.3. Mahalanobis Squared-Distance (MSD) -- 12.7.4. Singular Value Decomposition -- 12.7.5. Application to the Simulated Building Structure Data -- 12.8. Intelligent Feature Selection: A Projection Method -- 12.9. Cointegration -- 12.9.1. Theory -- 12.9.2. Illustration -- 12.10. Summary -- References -- 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.
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|a 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 -- 14. Damage Prognosis -- 14.1. Introduction.
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|a 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 -- 14.6.1. Damage Sensing Systems -- 14.6.2. Prediction Modelling for Future Loading Estimates -- 14.6.3. Model Verification and Validation -- 14.6.4. Reliability Analysis for Damage Prognosis Decision Making -- 14.7.A Prognosis Case Study: Crack Propagation in a Titanium Plate -- 14.7.1. The Computational Model -- 14.7.2. Monte Carlo Simulation -- 14.7.3. Issues -- 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.1.1. Basic Definitions -- A.1.2. Transducers, Sensors and Calibration -- A.1.3. Classification of Deterministic Signals.
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|a A.1.4. Classification of Random Signals -- A.2. Fourier Analysis and Spectra -- A.2.1. Fourier Series -- A.2.2. The Square Wave Revisited -- A.2.3.A First Look at Spectra -- A.2.4. The Exponential Form of the Fourier Series -- A.3. The Fourier Transform -- A.3.1. Basic Transform Theory -- A.3.2. An Interesting Function that is not a Function -- A.3.3. The Fourier Transform of a Periodic Function -- A.3.4. The Fourier Transform of a Pulse/Impulse -- A.3.5. The Convolution Theorem -- A.3.6. Parseval's Theorem -- A.3.7. The Effect of a Finite Time Window -- A.3.8. The Effect of Differentiation and Integration -- A.4. Frequency Response Functions and the Impulse Response -- A.4.1. Basic Definitions -- A.4.2. Harmonic Probing -- A.5. The Discrete Fourier Transform -- A.5.1. Basic Definitions -- A.5.2. More About Sampling -- A.5.3. The Fast Fourier Transform -- A.5.4. The DFT of a Sinusoid -- A.6. Practical Matters: Windows and Averaging -- A.6.1. Windows -- A.6.2. The Harris Test.
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|a A.6.3. Averaging and Power Spectral Density -- A.7. Correlations and Spectra -- A.8. FRF Estimation and Coherence -- A.8.1. FRF Estimation I -- A.8.2. The Coherence Function -- A.8.3. FRF Estimators II -- A.9. Wavelets -- A.9.1. Introduction and Continuous Wavelets -- A.9.2. Discrete and Orthogonal Wavelets -- A.10. Filters -- A.10.1. Introduction to Filters -- A.10.2.A Digital Low-Pass Filter -- A.10.3.A High-Pass Filter -- A.10.4.A Simple Classification of Filters -- A.10.5. Filter Design -- A.10.6. The Bilinear Transformation -- A.10.7. An Example of Digital Filter Design -- A.10.8.Combining Filters -- A.10.9. General Butterworth Filters -- A.11. System Identification -- A.11.1. Introduction -- A.11.2. Discrete-Time Models in the Frequency Domain -- A.11.3. Least-Squares Parameter Estimation -- A.11.4. Parameter Uncertainty -- A.11.5.A Case Study -- A.12. Summary -- References -- Appendix B Essential Linear Structural Dynamics.
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|a 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 -- B.6.1. Free, Undamped Motion -- B.6.2. Free, Damped Motion -- B.6.3. Forced, Damped Motion -- References.
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|a Structural health monitoring.
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|a TECHNOLOGY & ENGINEERING
|x Structural.
|2 bisacsh
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| 650 |
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|a Structural health monitoring.
|2 fast
|0 (OCoLC)fst01748414
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|a Electronic books.
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|a Electronic books.
|2 local
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|a Worden, K.
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|i Print version:
|a Farrar, C.R. (Charles R.).
|t Structural health monitoring.
|d Chichester, West Sussex, U.K. ; Hoboken, N.J. : Wiley, 2012
|z 9781119994336
|w (DLC) 2012018036
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| 856 |
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|u https://doi.org/10.1002/9781118443118
|z Full Text via HEAL-Link
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|b DG1
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