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The use of AI in predictive maintenance has become increasingly important as the global capacity of wind energy grows. By integrating AI into renewable energy systems, organizations can reduce costs and gain efficiencies. Real-time audio-based diagnostics with MongoDB Atlas Vector Search is a promising application for predictive maintenance of wind turbines. This approach involves capturing audio from equipment in different situations, converting the audio data to vectors, and uploading these vector embeddings to MongoDB Atlas. By comparing real-time audio clips with previously recorded sounds, an accurate prediction of the current status of the operation can be made. This method allows for real-time monitoring based on sound, reducing the risk of unexpected breakdowns and minimizing time and resources required for manual inspections.