Development of an Acoustic Sensing Based SHM Technique for Wind Turbine Blades

Wind turbine blades are exposed to continuously-varying aerodynamic forces, gravitational loads, lightning strikes, and weather conditions that lead the blade damage such as leading and trailing edge splits, cracks and holes. In this study, actively-controlled acoustic sources were utilized in order to excite the blade’s cavity structure from internal. The blade damage manifests itself in changes to the acoustic cavity frequency response functions and to the blade acoustic transmission loss. Proposed research examines the use of wireless sensing approach for detecting surface damage of the blades, while they are rotating when wind turbine is operational. A subscale wind turbine was built and used for carrying out preliminary experimental studies. Sensing system and strategy was benchmarked both using computational (FEM) model of the blades as well as the experimental results in the lab.

[1]  Bruno Fazenda,et al.  Acoustic condition monitoring of wind turbines: Tip faults , 2012 .

[2]  Heung-Fai Lam,et al.  System identification of an enclosure with leakages using a probabilistic approach , 2009 .

[3]  Mark A. Rumsey,et al.  Structural health monitoring of wind turbine blades , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[4]  Javad Baqersad,et al.  An acoustic-array based structural health monitoring technique for wind turbine blades , 2015, Smart Structures.

[5]  James A. Sherwood,et al.  Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing , 2014 .

[6]  Peter Avitabile,et al.  Dynamic Stress–Strain on Turbine Blades Using Digital Image Correlation Techniques Part 2: Dynamic Measurements , 2012 .

[7]  Anindya Ghoshal,et al.  Structural Health Monitoring Static Test of a Wind Turbine Blade , 2002 .

[8]  Vikas Arora,et al.  Acoustic-based damage detection method , 2014 .

[9]  Christopher Niezrecki,et al.  Wind turbine blade health monitoring using acoustic beamforming techniques , 2014 .

[10]  Daniel Rixen,et al.  Operational modal analysis of a 2.5 MW wind turbine using optical measurement techniques and strain gauges , 2013 .

[11]  Hoon Sohn,et al.  Crack detection technique for operating wind turbine blades using Vibro-Acoustic Modulation , 2014 .

[12]  James F. Manwell,et al.  Condition monitoring and prognosis of utility scale wind turbines , 2006 .

[13]  Daniel Rixen,et al.  Feasibility of monitoring large wind turbines using photogrammetry , 2010 .

[14]  Peter Avitabile,et al.  Dynamic Stress–Strain on Turbine Blade Using Digital Image Correlation Techniques Part 1: Static Load and Calibration , 2012 .

[15]  Bruno Fazenda Acoustic based condition monitoring of turbine blades , 2011 .

[16]  Daniel Rixen,et al.  Optical Measurements and Operational Modal Analysis on a Large Wind Turbine: Lessons Learned , 2011 .

[17]  Theodor Freiheit,et al.  Non-contact experimental modal analysis using air excitation and a microphone array , 2010 .

[18]  D. Rixen,et al.  Identification of the Dynamics of Large Wind Turbines by Using Photogrammetry , 2011 .

[19]  Ronald O. Stearman,et al.  Aircraft damage detection from acoustic and noise impressed signals found by a cockpit voice recorder , 1997 .

[20]  Elena Pierro,et al.  On the vibro-acoustical operational modal analysis of a helicopter cabin , 2009 .

[21]  Wenxian Yang Testing and condition monitoring of composite wind turbine blades , 2013 .

[22]  T. C. Chu,et al.  Three-dimensional displacement measurements using digital image correlation and photogrammic analysis , 1990 .