Cover for Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades

Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades

Book2023

Author:

Pengfei Liu

Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades

Book2023

 

Cover for Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades

Author:

Pengfei Liu

About the book

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Book description

Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades covers both the underlying theory and various techniques for effective structural ... read full description

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  1. Full text access
  2. Book chapterAbstract only

    1 - AE health monitoring technique for composite wind turbine blade: a state-of-art review

    Pages 1-37

  3. Book chapterAbstract only

    2 - AE tests and signal analysis of composite laminates with a central hole under tension

    Pages 39-52

  4. Book chapterAbstract only

    3 - AE tests and signal analysis on delamination of composite laminates under three-point bending and different temperature

    Pages 53-81

  5. Book chapterAbstract only

    4 - AE tests and signal analysis on delamination of composite laminates under three-point bending and hygrothermal environments

    Pages 83-130

  6. Book chapterAbstract only

    5 - AE tests and signal analysis of composite laminates under tensile strain rate loads

    Pages 131-144

  7. Book chapterAbstract only

    6 - AE tests and signal analysis on Delamination of composite laminates under tension–tension fatigue loads

    Pages 145-155

  8. Book chapterAbstract only

    7 - AE tests and damage mode identification of composite wind turbine blade under bending fatigue loads

    Pages 157-185

  9. Book chapterAbstract only

    8 - A waveform-based feature extraction model for AE signal analysis for structural health monitoring of composite wind turbine blade

    Pages 187-214

  10. Book chapterAbstract only

    9 - Damage mode identification of composite materials based on global AE data and clustering analysis

    Pages 215-252

  11. Book chapterAbstract only

    10 - AE feature extraction and signal representation model based on wavelet packet decomposition

    Pages 253-277

  12. Book chapterAbstract only

    11 - Dynamic feature evaluation and information mining for AE data stream

    Pages 279-302

  13. Book chapterAbstract only

    12 - Prediction model of residual load-bearing capacity of composite laminates using deep learning

    Pages 303-342

  14. Book chapterNo access

    Index

    Pages 343-351

About the book

Description

Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades covers both the underlying theory and various techniques for effective structural monitoring of composite wind turbine blades via acoustic emission signal analysis, helping readers solve critical problems such as noise elimination, defect detection, damage mode identification, and more. Author Pengfei Liu introduces techniques for identifying and analyzing progressive failure under tension, delamination, damage localization, adhesive composite joint failure, and other degradation phenomena, outlining methods such as time-difference, wavelet, machine learning, and more including combined methods.

The disadvantages and advantages of using each method are covered as are techniques for different blade-lengths and various blade substructures. Piezoelectric sensors are discussed as is experimental analysis of damage source localization. The book also takes great lengths to let readers know when techniques and concepts discussed can be applied to composite materials and structures beyond just wind turbine blades.

Acoustic Emission Signal Analysis and Damage Mode Identification of Composite Wind Turbine Blades covers both the underlying theory and various techniques for effective structural monitoring of composite wind turbine blades via acoustic emission signal analysis, helping readers solve critical problems such as noise elimination, defect detection, damage mode identification, and more. Author Pengfei Liu introduces techniques for identifying and analyzing progressive failure under tension, delamination, damage localization, adhesive composite joint failure, and other degradation phenomena, outlining methods such as time-difference, wavelet, machine learning, and more including combined methods.

The disadvantages and advantages of using each method are covered as are techniques for different blade-lengths and various blade substructures. Piezoelectric sensors are discussed as is experimental analysis of damage source localization. The book also takes great lengths to let readers know when techniques and concepts discussed can be applied to composite materials and structures beyond just wind turbine blades.

Key Features

  • Features fundamental acoustic emission theories and techniques for monitoring the structural integrity of wind turbine blades
  • Covers sensor arrangements, noise elimination, defect detection, and dominating damage mode identification using acoustic emission techniques
  • Outlines the wavelet method, the time-difference defect detection method, and damage mode identification techniques using machine learning
  • Discusses how the techniques covered can be extended and adapted for use in other composite structures under complex loads and in different environments
  • Features fundamental acoustic emission theories and techniques for monitoring the structural integrity of wind turbine blades
  • Covers sensor arrangements, noise elimination, defect detection, and dominating damage mode identification using acoustic emission techniques
  • Outlines the wavelet method, the time-difference defect detection method, and damage mode identification techniques using machine learning
  • Discusses how the techniques covered can be extended and adapted for use in other composite structures under complex loads and in different environments

Details

ISBN

978-0-323-88652-9

Language

English

Published

2023

Copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Imprint

Elsevier

Authors

Pengfei Liu

Ocean College, Zhejiang University, China