Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | European Workshop on Structural Health Monitoring |
| Editors | Piervincenzo Rizzo, Alberto Milazzo |
| Place of Publication | University of Palermo Palermo, Italy |
| Publisher | Springer International Publishing |
| Pages | 348-358 |
| Number of pages | 11 |
| Volume | 3 |
| Edition | 2022 |
| ISBN (Electronic) | 978-3-031-07322-9 |
| ISBN (Print) | 978-3-031-07321-2 |
| DOIs | |
| Publication status | Published - 22 Jun 2022 |
| Event | 10th European Workshop on Structural Health Monitoring - Palermo, Italy Duration: 4 Jul 2022 → 7 Jul 2022 Conference number: 10th https://www.ewshm2022.com/ |
Publication series
| Name | Lecture Notes in Civil Engineering |
|---|---|
| Volume | 270 LNCE |
| ISSN (Print) | 2366-2557 |
| ISSN (Electronic) | 2366-2565 |
Conference
| Conference | 10th European Workshop on Structural Health Monitoring |
|---|---|
| Abbreviated title | EWSHM2022 |
| Country/Territory | Italy |
| City | Palermo |
| Period | 4/07/22 → 7/07/22 |
| Internet address |
Keywords
- acceleration data-structur
- Offshore wind energy
- Autoencoder
- Novelty detection
- Machine Learning