Abstract
Wind turbine blades require continuous monitoring as they are a crucial part of the wind turbine system. Because of the surrounding climatic conditions and prolonged operation, wind turbine blades were exposed to severe vibrations. This leads to low productivity and failure in the future. The purpose of this research is to diagnose the condition of the wind turbine blades. The fault diagnosis is accomplished by artificial neural network and multiclass logistic regression using statistical features obtained from the vibrational signals. The faults are classified as pattern recognition problems, with three stages, namely feature extraction, selection and classification. In this work, statistical features were taken from vibration signals, and feature classification was performed using artificial neural network (ANN) and multiclass logistic regression algorithm. The results from both the techniques were compared based on classification accuracy percentage, and a superior model for real-time monitoring of wind turbine blades is suggested.
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Banala, H.S., Sahoo, S., Sethi, M.R., Sharma, A.K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_30
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DOI: https://doi.org/10.1007/978-981-19-5868-7_30
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