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

by A. Joshuva, V. Sugumaran

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.

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