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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.
|Title of host publication||Journal of Physics: Conference Series|
|Subtitle of host publication||Turbine Technology; Artificial Intelligence, Control and Monitoring|
|Number of pages||12|
|Publication status||Published - 2 Jun 2022|
|Event||TORQUE 2022: The Science of Making Torque from Wind (TORQUE 2022) - TU Delft, Delft, Netherlands|
Duration: 1 Jun 2022 → 3 Jun 2022
|Name||Journal of Physics: Conference Series|
|Period||1/06/22 → 3/06/22|
FingerprintDive into the research topics of 'Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.'. Together they form a unique fingerprint.
- 2 Active
VLADBC2: cSBO SOIL-TWIN: data driven design optimization and smart monitoring of monopile
Devriendt, C., Weijtjens, W., Sastre Jurado, C. & Stuyts, B.
1/01/20 → 30/09/23
- 1 Talk or presentation at a conference
Novelty detection in the Structural Health Monitoring data of an offshore wind turbine
Maximillian Francis Weil (Speaker)1 Aug 2022
Activity: Talk or presentation › Talk or presentation at a conference