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An Intelligent Detection Logic for Fan-Blade Damage to Wind Turbines Based on Mounted-Accelerometer Data

Auteur(s): ORCID
ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Buildings, , n. 10, v. 12
Page(s): 1588
DOI: 10.3390/buildings12101588
Abstrait:

Many wind turbines operate in harsh marine or shore environments. This study assists industry by establishing a real-time condition-monitoring and fault-detection system, with rules for recognizing a wind turbine’s abnormal operation mainly caused by different types of fan-blade damage. This system can ensure ideal wind turbine operation by monitoring the health status of the blades, detecting sudden anomalies, and performing maintenance almost in real time. This is especially significant for wind farms in areas subject to frequent natural disasters (e.g., earthquakes and typhoons). Turbines might fail to endure these because the manufacturers have built them according to the standards developed for areas less prone to natural disasters. The system’s rules are established by utilising concepts and methods from data analytics, digital signal processing (DSP) and statistics to analyse data from the accelerometer, which measures the vibration signals in three dimensions on the platform of the wind turbine’s base. The patterns for those cases involving fan-blade damage are found to establish the rules. With the anomalies detected and reported effectively, repairs and maintenance can be carried out on the faulty wind turbines. This enables ‘maintenance by prediction’ actions for unplanned maintenance as a supplement to the ‘predictive maintenance’ tasks for regular planned maintenance.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10700017
  • Publié(e) le:
    11.12.2022
  • Modifié(e) le:
    10.05.2023
 
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