Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.

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Samenvatting

Structural Health Monitoring (SHM) has seen an explosion in data gathering in the last few years. This is illustrated in the offshore wind industry through an increase in the amount of placed offshore wind turbines (OWT), a higher rate of SHM instrumented OWTs and an increase in the sampling rate. The growing data gathering has led to the interest of big data techniques in the SHM industry. This paper introduces a new more robust unsupervised novelty detection pipeline combining an autoencoder and the Mahalanobis distance and comparing this combination to both methods separately. This reduces the high dimensional input to a one dimensional novelty index for detecting anomalies in the OWT. Additionally the research considers the challenges that the downtime of non operationally essential sensors poses, a method is introduced to guarantee high model availability without losing the benefit of high fidelity.
Originele taal-2English
TitelJournal of Physics: Conference Series
SubtitelTurbine Technology; Artificial Intelligence, Control and Monitoring
Aantal pagina's12
Volume2265
Uitgave3
DOI's
StatusPublished - 2 jun 2022
EvenementTORQUE 2022: The Science of Making Torque from Wind (TORQUE 2022) - TU Delft, Delft, Netherlands
Duur: 1 jun 20223 jun 2022

Publicatie series

NaamJournal of Physics: Conference Series
ISSN van geprinte versie1742-6588

Conference

ConferenceTORQUE 2022
Land/RegioNetherlands
StadDelft
Periode1/06/223/06/22

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