Work

Acoustic inspection system with unmanned aerial vehicles for offshore wind turbines: A real case study

Year: 2025

Type: article

Abstract: • Novel NDT embedded in an UAV designed to acquire acoustic data from rotating wind turbine components. • Pre-processing and filtering methodologies based on Wavelet Transformn, Fast Fourier Transform, etc. • The methodology is tested using acoustic datasets acquired in an operating offshore wind turbine through different scenarios. • Consider external condition monitoring system and introduce noise records from simulated mechanical faults. • All the noise sources in all scenarios can be differentiated, demonstrating the high reliability and strength of the approach. Wind energy has become fundamental in the global transition towards renewable energies, with the deployment of larger and more complex wind turbines. CMS play a crucial role in early fault detection, enhancing productivity while decreasing downtimes and maintenance costs to ensure the optimal performance and viability of the wind energy industry. This paper presents a novel non-destructive testing system embedded in an unmanned aerial vehicle designed to acquire acoustic data from rotating wind turbine components. This approach develops pre-processing and filtering methodologies based on wavelet transform, Fast Fourier or energy transformation to avoid undesired noise sources, e.g., the rotor of the drones or the environment, and to obtain patterns associated with the real state of the wind turbine. The implementation of acoustic monitoring in wind turbines is a novelty in the current state of the art, and this methodology is tested in an operating offshore wind turbine. The experiments incorporate an external condition monitoring system and introduce noise records from simulated mechanical faults. The results demonstrate that all the noise sources and faulty and healthy scenarios can be differentiated, proving the reliability of the methodology and the robustness of the fault detection approach.

Source: Measurement

DOI: 10.1016/j.measurement.2025.117226

Authors: Isaac Segovia Ramírez, Fausto Pedro García Márquez, Pedro José Bernalte Sánchez, Alfredo Peinado Gonzalo

Institutions: Universidad Autónoma de Madrid, University of Castilla-La Mancha

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,760