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E-grāmata: Modeling and Estimation of Structural Damage [Wiley Online]

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  • Formāts: 450 pages
  • Izdošanas datums: 12-Feb-2016
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118776992
  • ISBN-13: 9781118776995
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  • Wiley Online
  • Cena: 142,59 €*
  • * this price gives unlimited concurrent access for unlimited time
Modeling and Estimation of Structural DamagePalielināt
  • Formāts: 450 pages
  • Izdošanas datums: 12-Feb-2016
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118776992
  • ISBN-13: 9781118776995
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118776995
Modelling and Estimation of Damage in Structures is a comprehensiveguide to solving the type of modelling and estimation problems associated with the physics of structural damage.





Provides a model-based approach to damage identification Presents an in-depth treatment of probability theory and random processes Covers both theory and algorithms for implementing maximum likelihood and Bayesian estimation approaches Includes experimental examples of all detection and identification approaches Provides a clear means by which acquired data can be used to make decisions regarding maintenance and usage of a structure
Preface xi
1 Introduction 1(20)
1.1 Users' Guide
1(1)
1.2 Modeling and Estimation Overview
2(2)
1.3 Motivation
4(3)
1.4 Structural Health Monitoring
7(10)
1.4.1 Data-Driven Approaches
10(4)
1.4.2 Physics-Based Approach
14(3)
1.5 Organization and Scope
17(1)
References
18(3)
2 Probability 21(30)
2.1 Probability Basics
23(2)
2.2 Probability Distributions
25(3)
2.3 Multivariate Distributions, Conditional Probability, and Independence
28(4)
2.4 Functions of Random Variables
32(7)
2.5 Expectations and Moments
39(4)
2.6 Moment-Generating Functions and Cumulants
43(6)
References
49(2)
3 Random Processes 51(44)
3.1 Properties of a Random Process
54(3)
3.2 Stationarity
57(4)
3.3 Spectral Analysis
61(20)
3.3.1 Spectral Representation of Deterministic Signals
62(3)
3.3.2 Spectral Representation of Stochastic Signals
65(2)
3.3.3 Power Spectral Density
67(4)
3.3.4 Relationship to Correlation Functions
71(3)
3.3.5 Higher Order Spectra
74(7)
3.4 Markov Models
81(1)
3.5 Information Theoretics
82(9)
3.5.1 Mutual Information
85(2)
3.5.2 Transfer Entropy
87(4)
3.6 Random Process Models for Structural Response Data
91(2)
References
93(2)
4 Modeling in Structural Dynamics 95(48)
4.1 Why Build Mathematical Models?
96(1)
4.2 Good Versus Bad Models — An Example
97(2)
4.3 Elements of Modeling
99(15)
4.3.1 Newton's Laws
101(1)
4.3.2 Background to Variational Methods
101(2)
4.3.3 Variational Mechanics
103(2)
4.3.4 Lagrange's Equations
105(3)
4.3.5 Hamilton's Principle
108(6)
4.4 Common Challenges
114(5)
4.4.1 Impact Problems
114(3)
4.4.2 Stress Singularities and Cracking
117(2)
4.5 Solution Techniques
119(14)
4.5.1 Analytical Techniques I — Ordinary Differential Equations
119(9)
4.5.2 Analytical Techniques II — Partial Differential Equations
128(3)
4.5.3 Local Discretizations
131(1)
4.5.4 Global Discretizations
132(1)
4.6 Volterra Series for Nonlinear Systems
133(7)
References
140(3)
5 Physics-Based Model Examples 143(60)
5.1 Imperfection Modeling in Plates
143(8)
5.1.1 Cracks as Imperfections
143(2)
5.1.2 Boundary Imperfections: In-Plane Slippage
145(6)
5.2 Delamination in a Composite Beam
151(9)
5.3 Bolted Joint Degradation: Quasi-static Approach
160(12)
5.3.1 The Model
161(3)
5.3.2 Experimental System and Procedure
164(2)
5.3.3 Results and Discussion
166(6)
5.4 Bolted Joint Degradation: Dynamic Approach
172(6)
5.5 Corrosion Damage
178(4)
5.6 Beam on a Tensionless Foundation
182(7)
5.6.1 Equilibrium Equations and Their Solutions
184(1)
5.6.2 Boundary Conditions
185(2)
5.6.3 Results
187(2)
5.7 Cracked, Axially Moving Wires
189(11)
5.7.1 Some Useful Concepts from Fracture Mechanics
191(2)
5.7.2 The Effect of a Crack on the Local Stiffness
193(1)
5.7.3 Limitations
194(2)
5.7.4 Equations of Motion
196(2)
5.7.5 Natural Frequencies and Stability
198(1)
5.7.6 Results
198(2)
References
200(3)
6 Estimating Statistical Properties of Structural Response Data 203(130)
6.1 Estimator Bias and Variance
206(3)
6.2 Method of Maximum Likelihood
209(4)
6.3 Ergodicity
213(5)
6.4 Power Spectral Density and Correlation Functions for LTI Systems
218(22)
6.4.1 Estimation of Power Spectral Density
218(16)
6.4.2 Estimation of Correlation Functions
234(6)
6.5 Estimating Higher Order Spectra
240(35)
6.5.1 Coherence Functions
246(2)
6.5.2 Bispectral Density Estimation
248(9)
6.5.3 Analytical Bicoherence for Non-Gaussian Signals
257(7)
6.5.4 Trispectral Density Function
264(11)
6.6 Estimation of Information Theoretics
275(9)
6.7 Generating Random Processes
284(18)
6.7.1 Review of Basic Concepts
285(2)
6.7.2 Data with a Known Covariance and Gaussian Marginal PDF
287(3)
6.7.3 Data with a Known Covariance and Arbitrary Marginal PDF
290(5)
6.7.4 Examples
295(7)
6.8 Stationarity Testing
302(10)
6.8.1 Reverse Arrangement Test
304(2)
6.8.2 Evolutionary Spectral Testing
306(6)
6.9 Hypothesis Testing and Intervals of Confidence
312(17)
6.9.1 Detection Strategies
313(6)
6.9.2 Detector Performance
319(8)
6.9.3 Intervals of Confidence
327(2)
References
329(4)
7 Parameter Estimation for Structural Systems 333(70)
7.1 Method of Maximum Likelihood
336(27)
7.1.1 Linear Least Squares
338(3)
7.1.2 Finite Element Model Updating
341(3)
7.1.3 Modified Differential Evolution for Obtaining MLEs
344(3)
7.1.4 Structural Damage MLE Example
347(5)
7.1.5 Estimating Time of Flight for Ultrasonic Applications
352(11)
7.2 Bayesian Estimation
363(29)
7.2.1 Conjugacy
365(1)
7.2.2 Using Conjugacy to Assess Algorithm Performance
366(8)
7.2.3 Markov Chain Monte Carlo (MCMC) Methods
374(5)
7.2.4 Gibbs Sampling
379(1)
7.2.5 Conditional Conjugacy: Sampling the Noise Variance
380(3)
7.2.6 Beam Example Revisited
383(3)
7.2.7 Population-Based MCMC
386(6)
7.3 Multimodel Inference
392(8)
7.3.1 Model Comparison via AIC
392(5)
7.3.2 Reversible Jump MCMC
397(3)
References
400(3)
8 Detecting Damage-Induced Nonlinearity 403(78)
8.1 Capturing Nonlinearity
407(8)
8.1.1 Higher Order Cumulants
408(2)
8.1.2 Higher Order Spectral Coefficients
410(2)
8.1.3 Nonlinear Prediction Error
412(2)
8.1.4 Information Theoretics
414(1)
8.2 Bolted Joint Revisited
415(6)
8.2.1 Composite Joint Experiment
415(2)
8.2.2 Kurtosis Results
417(2)
8.2.3 Spectral Results
419(2)
8.3 Bispectral Detection: The Single Degree-of-Freedom (SDOF), Gaussian Case
421(8)
8.3.1 Bispectral Detection Statistic
422(1)
8.3.2 Test Statistic Distribution
423(2)
8.3.3 Detector Performance
425(4)
8.4 Bispectral Detection: the General Multi-Degree-of-Freedom (MDOF) Case
429(9)
8.4.1 Bicoherence Detection Statistic Distribution
433(1)
8.4.2 Which Bicoherence to Compute?
434(2)
8.4.3 Optimal Input Probability Distribution for Detection
436(2)
8.5 Application of the HOS to Delamination Detection
438(6)
8.6 Method of Surrogate Data
444(7)
8.6.1 Fourier Transform-Based Surrogates
446(2)
8.6.2 AAFT Surrogates
448(1)
8.6.3 IAFFT Surrogates
449(1)
8.6.4 DFT Surrogates
450(1)
8.7 Numerical Surrogate Examples
451(13)
8.7.1 Detection of Bilinear Stiffness
451(5)
8.7.2 Detecting Cubic Stiffness
456(5)
8.7.3 Surrogate Invariance to Ambient Variation
461(3)
8.8 Surrogate Experiments
464(11)
8.8.1 Detection of Rotor — Stator Rub
465(2)
8.8.2 Bolted Joint Degradation with Ocean Wave Excitation
467(8)
8.9 Surrogates for Nonstationary Data
475(1)
8.10
Chapter Summary
476(2)
References
478(3)
9 Damage Identification 481(62)
9.1 Modeling and Identification of Imperfections in Shell Structures
481(20)
9.1.1 Modeling of Submerged Shell Structures
482(5)
9.1.2 Non-Contact Results Using Maximum Likelihood
487(5)
9.1.3 Bayesian Identification of Dents
492(9)
9.2 Modeling and Identification of Delamination
501(7)
9.3 Modeling and Identification of Cracked Structures
508(19)
9.3.1 Cracked Plate Model
508(2)
9.3.2 Crack Parameter Identification
510(13)
9.3.3 Optimization of Sensor Placement
523(4)
9.4 Modeling and Identification of Corrosion
527(11)
9.4.1 Experimental Setup
530(2)
9.4.2 Results and Discussion
532(6)
9.5
Chapter Summary
538(2)
References
540(3)
10 Decision Making in Condition-Based Maintenance 543(28)
10.1 Structured Decision Making
544(1)
10.2 Example: Ship in Transit
545(17)
10.2.1 Loading Data
547(5)
10.2.2 Ship "Stringer" Model
552(7)
10.2.3 Cumulative Fatigue Model
559(3)
10.3 Optimal Transit
562(6)
10.3.1 Problem Statement
562(1)
10.3.2 Solutions via Dynamic Programming
563(2)
10.3.3 Transit Examples
565(3)
10.4 Summary
568(1)
References
569(2)
Appendix A Useful Constants and Probability Distributions 571(4)
Appendix B Contour Integration of Spectral Density Functions 575(6)
Reference
580(1)
Appendix C Derivation of Terms for the Trispectrum of an MDOF Nonlinear Structure 581(6)
C.1 Simplification of CpijkVIII(τ1, τ2, τ3)
582(1)
C.2 Submanifold Terms in the Trispectrum
583(2)
C.3 Complete Trispectrum Expression
585(2)
Index 587
Jonathan M. Nichols received the B.Sc. degree from the University of Delaware in 1997 and the M. Sc. and Ph.D. degrees from Duke University in 1999 and 2002 respectively, all in Mechanical Engineering.?He is currently the Associate Superintendent for the Naval Research Laboratory Optical Sciences Division in Washington, D.C. His research interests include damage identification in structures, modelling and analysis of infrared imaging devices, signal and image processing, and parameter estimation. Kevin D. Murphy received the B.Sc. (Mechanical Engineering) and M. Sc. (Applied Mechanics) degrees from the University of Michigan in 1988 and 1990 respectively.?He received his Ph.D. from Duke University in 1994 in Mechanical Engineering.?He is currently a Professor and Mechanical Engineering Department Chair at the University of Louisville. His research focuses on the nonlinear mechanics, vibrations, and stability of structures for a broad variety of applications. Specific applications areas include: vibrations of damaged structures, adhesion/sticking contact in MEMS devices, and vibrations in manufacturing problems.
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