Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

Innes Murdo Black, Mark Richmond, Athanasios Kolios

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
154 Downloads (Pure)

Abstract

Information is key. Offshore wind farms are installed with supervisory control and data acquisition systems (SCADA) gathering valuable information. Determining the precise condition of an asset is essential on achieving the expected operational lifetime and efficiency. Equipment fault detection is necessary to achieve this. This paper presents a systematic literature review of machine learning methods applied to condition monitoring systems, using both vibration information and SCADA data together. Starting with conventional methods using vibration models, such as Fast-Fourier transforms to five prominent supervised learning regression models; Artificial neural network, support vector regression, Bayesian network, random forest and K-nearest neighbour. This review specifically looks at how conventional vibration data can be combined with SCADA data to determine the assets condition.

Original languageEnglish
Pages (from-to)923-946
Number of pages24
JournalInternational Journal of Sustainable Energy
Volume40
Issue number10
Early online date11 Mar 2021
DOIs
Publication statusPublished - 26 Nov 2021

Keywords

  • artificial intelligence
  • condition monitoring
  • machine-learning
  • SCADA
  • supervised learning

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