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

<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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Bibliographic Details
Main Author: Majdi Mansouri (16869885) (author)
Other Authors: Radhia Fezai (16869888) (author), Mohamed Trabelsi (16869891) (author), Mansour Hajji (16869894) (author), Mohamed-Faouzi Harkat (16869897) (author), Hazem Nounou (16869900) (author), Mohamed N. Nounou (16869903) (author), Kais Bouzrara (16869906) (author)
Published: 2020
Subjects:
Engineering
Electrical engineering
Environmental engineering
Data models
Data mining
Feature extraction
Feature extraction and selection
Fault detection and diagnosis (FDD)
Gaussian processes
Gaussian process regression (GPR)
Interval-valued data
Mathematical model
Radio frequency
Random forest (RF)
Wind turbines
Wind energy conversion (WEC) systems
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