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Analysis of Fault Diagnostic Technique in Wind Turbine Using Machine Leaning Models

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Analysis demonstrates a novel fault diagnostic technique in wind turbines, highlighting machine learning's role in condition monitoring.

The proposed technique predicts faults in wind turbines, improving reliability and reducing maintenance costs.
Using machine learning, this method classifies tribological faults like scuffing and overheating effectively.
By analyzing vibration signals, the technique enables early detection of faults before severe damage occurs.
Condition monitoring supports proactive maintenance strategies to enhance the overall availability of wind energy.

Kapoor et al. (2024) studied this question.

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