Structural Health Monitoring: A Machine Learning Perspective


Free download. Book file PDF easily for everyone and every device. You can download and read online Structural Health Monitoring: A Machine Learning Perspective file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Structural Health Monitoring: A Machine Learning Perspective book. Happy reading Structural Health Monitoring: A Machine Learning Perspective Bookeveryone. Download file Free Book PDF Structural Health Monitoring: A Machine Learning Perspective at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Structural Health Monitoring: A Machine Learning Perspective Pocket Guide.
Much more than documents.

Density Estimation -- 6. Extreme Value Statistics -- 6. Basic Theory -- 6. Determination of Limit Distributions -- 6. Dimension Reduction -- Principal Component Analysis -- 6. Simple Projection -- 6. Conclusions -- References -- 7. Damage-Sensitive Features. Waveform Comparisons -- 7.

Autocorrelation and Cross-Correlation Functions -- 7. The Coherence Function -- 7.

Shop by category

Some Remarks Regarding Waveforms and Spectra -- 7. Basic Signal Statistics -- 7.


  • Much more than documents..
  • Navigation Bar?
  • John F. Kennedy and the New Frontier - The rhetoric and the political results.
  • My Shopping Bag.
  • Sinful Miss Caroline Brooks.
  • Travailleur Immigre Raconte Ses Aventures (French Edition).
  • Structural health monitoring : a machine learning perspective (eBook, ) [loymaggblogasta.gq].

Transient Signals: Temporal Moments -- 7. Transient Signals: Decay Measures -- 7. Acoustic Emission Features -- 7. Preprocessing -- 7. Baseline Comparisons -- 7. Damage Localisation -- 7. Features Used with Impedance Measurements -- 7.

Join Kobo & start eReading today

Basic Modal Properties -- 7. Resonance Frequencies -- 7. Resonance Frequencies: The Forward Approach. Resonance Frequencies: Sensitivity Issues -- 7. Mode Shapes -- 7. Load-Dependent Ritz Vectors -- 7. Features Derived from Basic Modal Properties -- 7. Mode Shape Curvature -- 7. Modal Strain Energy -- 7. Modal Flexibility -- 7. Model Updating Approaches -- 7.

Objective Functions and Constraints -- 7. Direct Solution for the Modal Force Error -- 7. Optimal Matrix Update Methods -- 7.

Structural Health Monitoring A Machine Learning Perspective

Sensitivity-Based Update Methods -- 7. Eigenstructure Assignment Method -- 7. Hybrid Matrix Update Methods -- 7. Concluding Comment on Model Updating Approaches -- 7. Time Series Models -- 7.

Structural health monitoring of bridges: a model-free ANN-based approach to damage detection

Feature Selection -- 7. Sensitivity Analysis -- 7. Information Content -- 7. Assessment of Robustness -- 7. Optimisation Procedures -- 7. Metrics -- 7. Concluding Comments -- References -- 8. Features Based on Deviations from Linear Response -- 8. Coherence Function -- 8. Linearity and Reciprocity Checks -- 8. Harmonic Distortion -- 8. Frequency Response Function Distortions -- 8. Probability Density Function -- 8. Correlation Tests -- 8.

The Holder Exponent -- 8. Linear Time Series Prediction Errors -- 8. Nonlinear Time Series Models -- 8. Hilbert Transform -- 8. Nonlinear Acoustics Methods -- 8. Applications of Nonlinear Dynamical Systems Theory -- 8. Modelling a Cracked Beam as a Bilinear System -- 8. Chaotic Interrogation of a Damaged Beam -- 8.

Local Attractor Variance -- 8. Nonlinear System Identification Approaches -- 8. Restoring Force Surface Model -- 8. Machine Learning and Statistical Pattern Recognition -- 9. Introduction -- 9. Intelligent Damage Detection -- 9. Data Processing and Fusion for Damage Identification -- 9. Statistical Pattern Recognition: Hypothesis Testing -- 9. Statistical Pattern Recognition: General Frameworks -- 9. Discriminant Functions and Decision Boundaries -- 9.

Decision Trees -- 9.

Training -- Maximum Likelihood -- 9. Nearest Neighbour Classification -- 9.

See a Problem?

Analysis and Classification of the AE Data -- 9. Summary -- References -- Unsupervised Learning -- Novelty Detection -- Introduction -- The Experimental Structure and Data Capture -- Preprocessing of Data and Features -- Novelty Detection. Statistical Process Control -- Feature Extraction Based on Autoregressive Modelling -- The S Control Chart -- The Hotelling or Shewhart T2 Chart -- Thresholds for Novelty Detection -- Extreme Value Statistics -- Supervised Learning -- Classification and Regression -- Artificial Neural Networks -- Biological Motivation -- The Parallel Processing Paradigm -- The Artificial Neuron -- The Perceptron -- The Multilayer Perceptron -- Other Neural Network Structures.

Feedforward Networks -- Recurrent Networks -- Cellular Networks -- Statistical Learning Theory and Kernel Methods -- Structural Risk Minimisation -- Support Vector Machines -- Kernels -- Support Vector Regression -- Feature Selection Using Engineering Judgement -- Genetic Feature Selection -- Issues of Network Generalisation -- Discussion and Conclusions -- Discussion and Conclusions -- References -- Data Normalisation -- Sources of Environmental and Operational Variability -- Sensor System Design -- Modelling Operational and Environmental Variability -- Look-Up Tables.

Machine Learning Approaches to Data Normalisation -- Auto-Associative Neural Networks -- Factor Analysis -- Singular Value Decomposition -- Application to the Simulated Building Structure Data -- Cointegration -- Theory -- Illustration -- Fundamental Axioms of Structural Health Monitoring -- Axiom I.

Axiom II. Axiom III. Axiom IVa. Sensors Cannot Measure Damage. Axiom IVb. Axiom V. Axiom VI. Axiom VII. Axiom VIII. Damage Increases the Complexity of a Structure -- Damage Prognosis -- Motivation for Damage Prognosis -- The Current State of Damage Prognosis -- Defining the Damage Prognosis Problem -- The Damage Prognosis Process -- Damage Sensing Systems -- Prediction Modelling for Future Loading Estimates -- Model Verification and Validation -- The Computational Model -- Monte Carlo Simulation -- Issues -- Concluding Comments on Damage Prognosis -- Deterministic and Random Signals -- A.

Basic Definitions -- A. Transducers, Sensors and Calibration -- A. Classification of Deterministic Signals.

Structural Health Monitoring | Wiley Online Books

Classification of Random Signals -- A. Fourier Analysis and Spectra -- A. Fourier Series -- A. The Square Wave Revisited -- A. A First Look at Spectra -- A. The Fourier Transform -- A.

Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective
Structural Health Monitoring: A Machine Learning Perspective Structural Health Monitoring: A Machine Learning Perspective

Related Structural Health Monitoring: A Machine Learning Perspective



Copyright 2019 - All Right Reserved