A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

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.