Open Access
ARTICLE
Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach
A. Joshuva1,*, V. Sugumaran2
1 Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India
2 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur-Kelambakkam Road, Chennai 600127, India
* Corresponding Author: A. Joshuva. Email:
Structural Durability & Health Monitoring 2019, 13(4), 399-416. https://doi.org/10.32604/sdhm.2019.03014
Abstract
Wind energy is considered as a alternative renewable energy source due
to its low operating cost when compared with other sources. The wind turbine is
an essential system used to change kinetic energy into electrical energy. Wind turbine
blades, in particular, require a competitive condition inspection approach as
it is a significant component of the wind turbine system that costs around 20-25
percent of the total turbine cost. The main objective of this study is to differentiate
between various blade faults which affect the wind turbine blade under operating
conditions using a machine learning approach through histogram features. In this
study, blade bend, hub-blade loose connection, blade erosion, pitch angle twist,
and blade cracks were simulated on the blade. This problem is formulated as a
machine learning problem which consists of three phases, namely feature extraction,
feature selection and feature classification. Histogram features are extracted
from vibration signals and feature selection was carried out using the J48 decision
tree algorithm. Feature classification was performed using 15 tree classifiers. The
results of the machine learning classifiers were compared with respect to their
accuracy percentage and a better model is suggested for real-time monitoring
of a wind turbine blade.
Keywords
Cite This Article
APA Style
Joshuva, A., Sugumaran, V. (2019). Comparative study on tree classifiers for application to condition monitoring of wind turbine blade through histogram features using vibration signals: A data-mining approach. Structural Durability & Health Monitoring, 13(4), 399-416. https://doi.org/10.32604/sdhm.2019.03014
Vancouver Style
Joshuva A, Sugumaran V. Comparative study on tree classifiers for application to condition monitoring of wind turbine blade through histogram features using vibration signals: A data-mining approach. Structural Durability Health Monit . 2019;13(4):399-416 https://doi.org/10.32604/sdhm.2019.03014
IEEE Style
A. Joshuva and V. Sugumaran, "Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach," Structural Durability Health Monit. , vol. 13, no. 4, pp. 399-416. 2019. https://doi.org/10.32604/sdhm.2019.03014
Citations