An experimental study on the data-driven structural health monitoring of large wind turbine blades using a single accelerometer and actuator

David García, Dmitri Tcherniak

Research output: Contribution to journalArticle

8 Citations (Scopus)
3 Downloads (Pure)

Abstract

The study aimed to investigate the performance of a Structural Health Monitoring (SHM) methodology based on use of a single accelerometer and single actuator in the detection and monitoring of the growth of the damage in the trailing edge of a wind turbine blade. The study used a data-driven vibration SHM, which is considered as a simple, nonparametric method for data compression and information extraction. The methodology found combinations of variables that describe major trends and fluctuations within the vibration response measured on the structure to create a reference state to which new observations were evaluated for damage diagnosis. The blade was artificially excited with an electromechanical actuator that introduced a mechanical impulse in the blade. The vibration responses were measured by accelerometers distributed along the trailing and leading edge of the wind turbine blade. The rationale behind this study was to investigate the combination of accelerometer/actuator location for damage detection sensitivity and damage progression when a single accelerometer and single actuator was used. The experimental study was conducted on an SSP 34 m wind turbine blade with and without introduced damage. Different damage sizes were also considered to evaluate the detectability of the damage. This study complements previous analyses in the same blade where studies on the effect of damage in modal parameters and a multiple sensor SHM technique were evaluated. The results demonstrated that the methodology was able to detect different damage sizes by using only one accelerometer. It was also demonstrated that damage detection and damage progression is affected by the accelerometer/actuator position but this effect is used to provide a rough information about damage location.
Original languageEnglish
Pages (from-to)102-119
Number of pages18
JournalMechanical Systems and Signal Processing
Volume127
Early online date6 Mar 2019
DOIs
Publication statusPublished - 15 Jul 2019

Keywords

  • data-driven techniques
  • singular spectrum analysis
  • wind turbine blade
  • structural health monitoring (SHM)
  • damage blade assessment

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