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Wind Turbine Blade Fault Diagnosis Using Vibration Signals through Decision Tree Algorithm
Objectives: The main objective of this research is to develop a model which can able to predict the various blade faults occurs in the wind turbine blade while the turbine in operating condition using vibration signals. Method: This study is considered as a machine learning problem which consist of three phases, namely feature extraction, feature selection and feature classification. In this research, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and different parameters of J48 algorithm were optimized to build a better classifier. Findings: In this study, the J48 algorithm was used and the classification accuracy was found to be 85.33% for multiclass problem. This is a novel approach of finding the different problem occurs in wind turbine blade at once. Improvements: This algorithm is applicable for real-time analysis and further the condition monitoring can be made as a portable device with less computation time.
Fault Diagnosis, J48 Algorithm, Statistical Feature, Structural Health Monitoring, Vibration Signal, Wind Turbine Blade.
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