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
http://hdl.handle.net/10993/46762
A fault detector/classifier for closed-ring power generators using machine learning
English
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 []
2021
Reliability Engineering and System Safety
Elsevier
Yes
International
0951-8320
1879-0836
Oxford
Netherlands
[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.
http://hdl.handle.net/10993/46762
10.1016/j.ress.2021.107614

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
Elsevier2021-AuthorPostPrint.pdfAuthor postprint594.55 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.