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Anomaly Detection in Vibration Signals for Structural Health Monitoring of an Offshore Wind Turbine

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

The current approach for detecting anomalies in acceleration
signals relies extensively on feature engineering. Indeed, detecting rotor
imbalances in wind turbines starts by first isolating and then assessing
the energy of the 1P harmonic, leading to a feature that is efficient but
not failure mode agnostic. While different engineered features can be used
concurrently, some anomalies in the acceleration signal might remain
undetected by the algorithm, even though they are visually noticeable
to a human in the signal’s spectrogram. Thus, this project aims to build
an AI algorithm capable of detecting anomalies in spectrograms, agnostic of their origin, providing an early warning for potential structural
issues. The proposed algorithm infers spectrograms of acceleration signals through a deep autoencoder. Anomalies are identified based on a
custom reconstruction error. A sensitivity analysis is performed for two
types of anomaly, in which waveforms with different energy levels are artificially added to an acceleration signal measured from an offshore wind
turbine (OWT). For a 1P harmonic anomaly representing 20% of the
total signal energy, the proposed approach yielded an efficiency (AUC)
equal to 96% thanks to a novel reconstruction error, which significantly
increased the performances.
Boek: European Workshop on Structural Health Monitoring
Edition: 2022
Series: Lecture Notes in Civil Engineering
Volume: 3
Pagina's: 348-358
Aantal pagina's: 11
Jaar van publicatie:2022
Trefwoorden:acceleration data-structur, Offshore wind energy, Autoencoder, Novelty detection, Machine Learning