Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.
Skip Nav Destination
ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 21–24, 2016
Charlotte, North Carolina, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-5020-6
PROCEEDINGS PAPER
Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms Available to Purchase
Taylor Regan,
Taylor Regan
University of Massachusetts Lowell, Lowell, MA
Search for other works by this author on:
Rukiye Canturk,
Rukiye Canturk
University of Massachusetts Lowell, Lowell, MA
Search for other works by this author on:
Elizabeth Slavkovsky,
Elizabeth Slavkovsky
University of Massachusetts Lowell, Lowell, MA
Search for other works by this author on:
Christopher Niezrecki,
Christopher Niezrecki
University of Massachusetts Lowell, Lowell, MA
Search for other works by this author on:
Murat Inalpolat
Murat Inalpolat
University of Massachusetts Lowell, Lowell, MA
Search for other works by this author on:
Taylor Regan
University of Massachusetts Lowell, Lowell, MA
Rukiye Canturk
University of Massachusetts Lowell, Lowell, MA
Elizabeth Slavkovsky
University of Massachusetts Lowell, Lowell, MA
Christopher Niezrecki
University of Massachusetts Lowell, Lowell, MA
Murat Inalpolat
University of Massachusetts Lowell, Lowell, MA
Paper No:
DETC2016-59686, V008T10A040; 10 pages
Published Online:
December 5, 2016
Citation
Regan, T, Canturk, R, Slavkovsky, E, Niezrecki, C, & Inalpolat, M. "Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms." Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 8: 28th Conference on Mechanical Vibration and Noise. Charlotte, North Carolina, USA. August 21–24, 2016. V008T10A040. ASME. https://doi.org/10.1115/DETC2016-59686
Download citation file:
72
Views
Related Proceedings Papers
Related Articles
Optimized Constant-Life Diagram for the Analysis of Fiberglass Composites Used in Wind Turbine Blades
J. Sol. Energy Eng (November,2005)
Machine Learning and Anomaly Detection Algorithms for Damage Characterization From Compliance Data in Three-Point Bending Fatigue
ASME J Nondestructive Evaluation (November,2021)
Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms
J. Vib. Acoust (December,2017)
Related Chapters
Colorectal Cancer Prognosis in Gene Expression Data
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Introduction
Computer Vision for Structural Dynamics and Health Monitoring
Digital Transformation by the Implementation of the True Digital Twin Concept and Big Data Technology for Structural Integrity Management
Ageing and Life Extension of Offshore Facilities