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Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

A. Joshuva1, V. Sugumaran2
Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, 603103, India.
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(2), 181-203.


Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer. The statistical, histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.


Structural health monitoring, fault diagnosis, blade crack, statistical features, histogram features, ARMA features, tree algorithms, vibration signals.

Cite This Article

Joshuva, A., Sugumaran, V. (2019). Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach. Structural Durability & Health Monitoring, 13(2), 181–203.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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