Adaptive Feature Selection for Enhancing Blade Damage Diagnosis on an Operational Wind Turbine

Artur Movsessian*, David Garcia, Dmitri Tcherniak

*Corresponding author for this work

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

Abstract / Description of output

Monitoring wind turbine blades (WTB) is an important aspect when assessing the health of wind turbines. Structural Health Monitoring (SHM) systems enable continuous monitoring of the condition of WTB during operation. When SHM is coupled with advanced data analysis techniques, damage detection can be improved by customizing the methodologies to the structure being monitored. The work presented in this manuscript introduces an SHM methodology based on a Semi-supervised damage detection algorithm that uses preliminary findings to reinforce the selection of features used to identify damage. The methodology proposes a novel technique for feature extraction by sorting the acceleration values in each vibration response. Then, an adaptive feature selection algorithm is applied to identify the most sensitive characteristics of the feature for damage detection. This technique enhances the correlation between measurements of the same blade status and therefore the performance of the proposed SHM methodology. The methodology was implemented on real acceleration measurements on an operational Vestas V27 WTB. The results were compared with those from an alternative Semi-supervised methodology that considers only the measurements from the undamaged WTB. The comparison of the results demonstrated that the proposed adaptive feature selection algorithm enhances damage diagnosis.

Original languageEnglish
Title of host publicationIntelligent Manufacturing and Mechatronics - Proceedings of the 2nd Symposium on Intelligent Manufacturing and Mechatronics – SympoSIMM 2019
EditorsMagd Abdel Wahab, Zamberi Jamaludin, Mohd Najib Ali Mokhtar
PublisherPleiades Publishing
Number of pages12
ISBN (Print)9789811383304, 9789811395383
Publication statusE-pub ahead of print - 5 Jul 2019
Event13th International Conference on Damage Assessment of Structures, DAMAS 2019 - Porto, Portugal
Duration: 9 Jul 201910 Jul 2019

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364


Conference13th International Conference on Damage Assessment of Structures, DAMAS 2019

Keywords / Materials (for Non-textual outputs)

  • Adaptive feature algorithm
  • Damage detection
  • Feature selection
  • Semi-supervised learning
  • Structural Health Monitoring
  • Wind turbine blades


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