CASU2Net: Cascaded Unification Network by a Two-step Early Fusion for
Fault Detection in Offshore Wind Turbines
- URL: http://arxiv.org/abs/2011.12130v3
- Date: Thu, 26 Aug 2021 15:24:03 GMT
- Title: CASU2Net: Cascaded Unification Network by a Two-step Early Fusion for
Fault Detection in Offshore Wind Turbines
- Authors: Soorena Salari and Nasser Sadati
- Abstract summary: This paper presents a novel feature fusion-based deep learning model (called CASU2Net) for fault detection in offshore wind turbines.
We use five sensors and a sliding window to exploit the inherent temporal information contained in the raw time-series data obtained from sensors.
The proposed model uses the nonlinear relationships among multiple sensor variables and the temporal dependency of each sensor on others which considerably increases the performance of fault detection model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a novel feature fusion-based deep learning model (called
CASU2Net) for fault detection in offshore wind turbines. The proposed CASU2Net
model benefits of a two-step early fusion to enrich features in the final
stage. Moreover, since previous studies did not consider uncertainty while
model developing and also predictions, we take advantage of Monte Carlo dropout
(MC dropout) to enhance the certainty of the results. To design fault detection
model, we use five sensors and a sliding window to exploit the inherent
temporal information contained in the raw time-series data obtained from
sensors. The proposed model uses the nonlinear relationships among multiple
sensor variables and the temporal dependency of each sensor on others which
considerably increases the performance of fault detection model. A 10-fold
cross-validation approach is used to verify the generalization of the model and
evaluate the classification metrics. To evaluate the performance of the model,
simulated data from a benchmark floating offshore wind turbine (FOWT) with
supervisory control and data acquisition (SCADA) are used. The results
illustrate that the proposed model would accurately disclose and classify more
than 99% of the faults. Moreover, it is generalizable and can be used to detect
faults for different types of systems.
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