Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring

Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes. This article proposes a novel non-destructive testing system to analyse acoustically rotatory devices of wind turbines. It captures the noise emitted by the devices using an acoustic condition monitoring system embedded in an unmanned aerial vehicle. The signal acquired is sent to ground computer station for recording and analysing the data. It uses a test rig, previously validated, to carry out a set of experiments to simulate the main faults. A signal processing method is done by wavelet transforms that filters and analyses the energy patterns of the signals. The results are analysed qualitatively and quantitatively considering different scenarios. A statistical analysis is developed to compare the numerical results provided by different wavelet transform families and convolutional neural network. It is concluded that Symlets and Daubechies families report equivalent results for this case study. The accuracies of the results are more than 75%, reaching up to 100%. The approach is validated employing Friedman test.

• Record URL:
  • https://doi.org/10.1177/14759217211004822
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14759217
• Supplemental Notes:
  • © 2021 Fausto Pedro García Márquez et al.
• Authors:
  • García Márquez, Fausto Pedro
  • 0000-0002-9245-440X
  • Bernalte Sánchez, Pedro José
  • Segovia Ramírez, Isaac
• Publication Date: 2022-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 485-500
• Serial:
  • Structural Health Monitoring
  • Volume: 21
  • Issue Number: 2
  • Publisher: Sage Publications, Incorporated
  • ISSN: 1475-9217
  • EISSN: 1741-3168
  • Serial URL: http://shm.sagepub.com/

Subject/Index Terms

• TRT Terms: Acoustic emission tests; Data analysis; Drone radar; Signal processing; Structural health monitoring; Wind power generation
• Subject Areas: Aviation; Data and Information Technology; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01843621
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 25 2022 10:07AM