Wind energy is emerging as a leading renewable energy source, with the deployment of large and more innovative wind turbines (WTs). This expansion requires new condition monitoring systems (CMS) and diagnostic techniques to reach competitiveness, improve reliability and availability, and minimize maintenance costs associated with WT operations. This research proposes a novel CMS based on acoustic analysis of WTs, combined with advanced analytics for pattern recognition. An acoustic CMS embedded in an unmanned aerial vehicle is developed to capture, send, and process the sound emitted in the nacelle to an acoustic receiver by a ground station for further analysis to assess the viability of the methodology. The article presents initial results from the laboratory using the fast Fourier transform algorithm, studying the signals in the time-frequency domain aspect and measuring the energy. An advanced signal processing method is presented to filter and define patterns that identify the state and condition of different proposed scenarios. The methodology is tested in a working WT, and the results demonstrate that the acoustic analysis is suitable for maintenance management in WTs.
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