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

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. - 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

He is an influential researcher in the fields of damage mechanics, fracture mechanics, and finite element analysis of lightweight composite structures. He has authored over 60 scientific publications, has trained over 10 graduate students in these fields, and his research has been cited over 1200 times. He has developed a number of damage models on intralaminar and interlaminar failure properties of laminated composite structures under quasi-static tension, compression, and shear loads. He has received Chinese Natural Science funding three times.

---

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. - 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