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

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...

Full description

Bibliographic Details
Main Authors: Majdi Mansouri, Radhia Fezai, Mohamed Trabelsi, Mansour Hajji, Mohamed-Faouzi Harkat, Hazem Nounou, Mohamed N. Nounou, Kais Bouzrara
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Gaussian process regression (GPR)
interval-valued data
random forest (RF)
feature extraction and selection
fault detection and diagnosis (FDD)
wind energy conversion (WEC) systems
Online Access:https://ieeexplore.ieee.org/document/9276468/

Internet

https://ieeexplore.ieee.org/document/9276468/

Similar Items