A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems
<p>Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) techni...
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2020
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| Summary: | <p>Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.</p> <h3>Other Information</h3> <p>Published in: IEEE Access<br> License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3042101" target="_blank">https://dx.doi.org/10.1109/access.2020.3042101</a></p> |
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