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

Onderzoeksoutput: Conference paperResearch

2 Citaten (Scopus)
146 Downloads (Pure)


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.
Originele taal-2English
TitelEuropean Workshop on Structural Health Monitoring
RedacteurenPiervincenzo Rizzo, Alberto Milazzo
Plaats van productieUniversity of Palermo Palermo, Italy
UitgeverijSpringer International Publishing
Aantal pagina's11
ISBN van elektronische versie978-3-031-07322-9
ISBN van geprinte versie978-3-031-07321-2
StatusPublished - 22 jun 2022
Evenement10th European Workshop on Structural Health Monitoring - Palermo, Italy
Duur: 4 jul 20227 jul 2022
Congresnummer: 10th

Publicatie series

NaamLecture Notes in Civil Engineering
Volume270 LNCE
ISSN van geprinte versie2366-2557
ISSN van elektronische versie2366-2565


Conference10th European Workshop on Structural Health Monitoring
Verkorte titelEWSHM2022
Internet adres


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