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.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Preface - p. ix 6. 1. AE health monitoring technique for composite wind turbine blade: a state-of-art review - p. 1 7. 2. AE tests and signal analysis of composite laminates with a central hole under tension - p. 39 8. 3. AE tests and signal analysis on delamination of composite laminates under three-point bending and different temperature - p. 53 9. 4. AE tests and signal analysis on delamination of composite laminates under three-point bending and hygrothermal environments - p. 83 10. 5. AE tests and signal analysis of composite laminates under tensile strain rate loads - p. 131 11. 6. AE tests and signal analysis on Delamination of composite laminates under tension–tension fatigue loads - p. 145 12. 7. AE tests and damage mode identification of composite wind turbine blade under bending fatigue loads - p. 157 13. 8. A waveform-based feature extraction model for AE signal analysis for structural health monitoring of composite wind turbine blade - p. 187 14. 9. Damage mode identification of composite materials based on global AE data and clustering analysis - p. 215 15. 10. AE feature extraction and signal representation model based on wavelet packet decomposition - p. 253 16. 11. Dynamic feature evaluation and information mining for AE data stream - p. 279 17. 12. Prediction model of residual load-bearing capacity of composite laminates using deep learning - p. 303 18. Index - p. 343
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.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Preface - p. ix 6. 1. AE health monitoring technique for composite wind turbine blade: a state-of-art review - p. 1 7. 2. AE tests and signal analysis of composite laminates with a central hole under tension - p. 39 8. 3. AE tests and signal analysis on delamination of composite laminates under three-point bending and different temperature - p. 53 9. 4. AE tests and signal analysis on delamination of composite laminates under three-point bending and hygrothermal environments - p. 83 10. 5. AE tests and signal analysis of composite laminates under tensile strain rate loads - p. 131 11. 6. AE tests and signal analysis on Delamination of composite laminates under tension–tension fatigue loads - p. 145 12. 7. AE tests and damage mode identification of composite wind turbine blade under bending fatigue loads - p. 157 13. 8. A waveform-based feature extraction model for AE signal analysis for structural health monitoring of composite wind turbine blade - p. 187 14. 9. Damage mode identification of composite materials based on global AE data and clustering analysis - p. 215 15. 10. AE feature extraction and signal representation model based on wavelet packet decomposition - p. 253 16. 11. Dynamic feature evaluation and information mining for AE data stream - p. 279 17. 12. Prediction model of residual load-bearing capacity of composite laminates using deep learning - p. 303 18. Index - p. 343
