A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

  • Authors

    • A. Joshuva
    • V. Sugumaran
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20833
  • Condition monitoring, wind turbine blade, statistical features, machine learning, vibration signals.

  • Abstract

    This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.

     

     

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  • How to Cite

    Joshuva, A., & Sugumaran, V. (2018). A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach. International Journal of Engineering & Technology, 7(4.10), 190-196. https://doi.org/10.14419/ijet.v7i4.10.20833

    Received date: 2018-10-03

    Accepted date: 2018-10-03

    Published date: 2018-10-02