Machine Learning Analysis of Non-Destructive Evaluation Data from Radar Inspection of Wind Turbine Blades

Wenshuo Tang, Daniel Mitchell, Jamie Blanche, Ranjeetkumar Gupta, David Flynn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Wind Turbines are vital contributors to powering the world with renewable energy. As the wind energy sector grows, the reliability and resilience of wind turbine systems becomes increasingly important. One of the most important components, the wind turbine blades, are typically inspected with visual analysis. This is insufficient for providing detailed, consistent, and readily accessible surface and sub-surface analysis for Structural Health Monitoring (SHM). In this paper we present a novel method of Non-Destructive Evaluation (NDE) for wind turbine blades by utilizing Frequency Modulated Continuous Wave (FMCW) radar sensing with machine learning analytics. By utilizing machine learning models on FMCW radar return signal amplitude (RSA) collected from different turbine blade samples, our results demonstrate that we can classify blade types by composition, and diameter differentials of 3 millimeters with over 95% classification accuracy. Thus, our methodology presents an insight to a promising SHM-NDE solution for surface and sub­surface characterization of wind turbine blades and other composite structures.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
PublisherIEEE
Pages122-128
Number of pages7
ISBN (Electronic)9781665449762
DOIs
Publication statusPublished - 19 Oct 2021
Event2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control - Weihai, China
Duration: 13 Aug 202115 Aug 2021

Conference

Conference2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control
Abbreviated titleSDPC 2021
Country/TerritoryChina
CityWeihai
Period13/08/2115/08/21

Keywords

  • FMCW Radar
  • Non-Destructive Evaluation
  • Wind Turbine blade
  • Machine Learning
  • Asset Integrity Management

Fingerprint

Dive into the research topics of 'Machine Learning Analysis of Non-Destructive Evaluation Data from Radar Inspection of Wind Turbine Blades'. Together they form a unique fingerprint.

Cite this