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Crack Detection and Diagnosis for Wind Turbines Using Naive

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Document pages: 6 pages

Abstract: Wind control is depicting the procedure by which wind is utilized to create electricity. It is a renewable source of energy that helps to cut down the pollution. The drawback in wind energy generation is the high maintenance costs associated with mechanical faults. Keeping in mind the end goal to decrease these effects have been incorporated blame discovery framework in wind turbines known as Fault identification and Diagnosis framework. The proposed strategy is a Naive Bayes classifier identification framework to recognize the wind turbine s split exactness. This framework depends on probabilistic classifier with solid impedance presumption of each blame case. The fundamental thought is to utilize a specific number of Bayes classifiers to manages diverse kinds of shortcomings influencing the wind turbine. The distinctive process was researched to recognize the breaks in the breeze turbine. The recreation comes about to demonstrate the best exhibitions of the proposed approach.

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