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Audible Noise Evaluation in Wind Turbines Through Artificial Intelligence Techniques.

Mathaus Ferreira da Silva¹, Juliano Emir Nunes Masson¹, Murillo Ferreira Dos Santos²

¹Robotictech Technology Services, Juiz de Fora 36036-230, Brazil.

Sensors (Basel, Switzerland)

|March 17, 2025

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This study introduces an AI model for wind turbine failure detection using sound analysis. The system identifies anomalies in component sounds, enabling predictive maintenance and improving operational reliability.

Area of Science:

Engineering
Artificial Intelligence
Acoustics

Background:

Wind power is a key renewable energy source, necessitating reliable operation.
Predictive maintenance is crucial for wind turbine availability and longevity.
Component failures can lead to costly downtime and safety risks.

Purpose of the Study:

To develop an AI-driven method for detecting wind turbine failures using acoustic emissions.
To enhance the reliability and availability of wind power systems through early fault identification.
To create a robust system for preventive maintenance in wind energy infrastructure.

Main Methods:

An Artificial Intelligence (AI) model employing unsupervised learning and image processing.
Analysis of acoustic spectrograms from wind turbine components to establish baseline 'healthy' conditions.

Keywords:

artificial intelligence audible noise wind turbine

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Reconstruction of current acoustic data to detect deviations indicating potential failures.

A supervised learning specialist network for identifying specific failure events from anomalous data.

Main Results:

The AI model successfully identified known faults in five tested wind turbines.
Low similarity between reconstructed and input data indicated specific component failures.
The system demonstrated effectiveness in capturing anomalies indicative of potential issues.
Satisfactory results were achieved in detecting faults through acoustic signature analysis.

Conclusions:

The proposed AI method offers a viable solution for acoustic-based failure detection in wind turbines.
This approach supports effective preventive maintenance strategies, enhancing operational efficiency.
The technology has potential for broader application in monitoring other industrial machinery with rotating components.