Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Cevasco, D and Tautz-Weinert, J and Kolios, A J and Smolka, U (2020) Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data. Journal of Physics: Conference Series, 1618 (2). 022063. ISSN 1742-6588

[img]
Preview
Text (Cevasco-etal-JPCS-2020-Applicability-of-machine-learning-approaches-for-structural-damage)
Cevasco_etal_JPCS_2020_Applicability_of_machine_learning_approaches_for_structural_damage.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (719kB)| Preview

    Abstract

    Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.