Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

D. Cevasco, J. Tautz-Weinert, U. Smolka, A. Kolios

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Abstract

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine's SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only.

Original languageEnglish
Article number012021
JournalJournal of Physics: Conference Series
Volume1669
Issue number1
DOIs
Publication statusPublished - 26 Oct 2020
Event17th Deep Sea Offshore Wind R and D Conference, DeepWind 2020 - Trondheim, Norway
Duration: 15 Jan 202017 Jan 2020

Keywords

  • structural damage detection
  • Supervisory Control and Data Acquisition
  • SCADA
  • wind turbines
  • offshore wind farm management

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