Reference : A fault detector/classifier for closed-ring power generators using machine learning
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
A fault detector/classifier for closed-ring power generators using machine learning
Quintanilha, Igor M. mailto []
Elias, Vitor R. M. mailto []
Silva, Felipe B. mailto []
Fonini, Pedro A. M. mailto []
Silva, Eduardo A. B. mailto []
Netto, Sergio L. mailto []
Apolinário Jr., José A. mailto []
Campos, Marcello L. R. mailto []
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Wold, Lars E. mailto []
Andersen, Rune B. mailto []
Reliability Engineering and System Safety
Yes (verified by ORBilu)
[en] Condition-based monitoring ; Detection ; Classification ; Machine learning ; Principal components ; Random forests
[en] Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and reducing operational costs. Most such systems employ expertise-reliant rule-based methods. This work proposes a different framework, in which machine-learning algorithms are used for detecting and classifying several fault types in a power-generation system of dynamically positioned vessels. First, principal component analysis is used to extract relevant information from labeled data. A random-forest algorithm then learns hidden patterns from faulty behavior in order to infer fault detection from unlabeled data. Results on fault detection and classification for the proposed approach show significant improvement on accuracy and speed when compared to results from rule-based methods over a comprehensive database.

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