Matches in SemOpenAlex for { <https://semopenalex.org/work/W3150874090> ?p ?o ?g. }
- W3150874090 endingPage "107614" @default.
- W3150874090 startingPage "107614" @default.
- W3150874090 abstract "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." @default.
- W3150874090 created "2021-04-13" @default.
- W3150874090 creator A5002277423 @default.
- W3150874090 creator A5013942581 @default.
- W3150874090 creator A5019302005 @default.
- W3150874090 creator A5043514538 @default.
- W3150874090 creator A5049141644 @default.
- W3150874090 creator A5052025135 @default.
- W3150874090 creator A5055704117 @default.
- W3150874090 creator A5057872133 @default.
- W3150874090 creator A5061595221 @default.
- W3150874090 creator A5068109451 @default.
- W3150874090 creator A5081219742 @default.
- W3150874090 date "2021-08-01" @default.
- W3150874090 modified "2023-09-23" @default.
- W3150874090 title "A fault detector/classifier for closed-ring power generators using machine learning" @default.
- W3150874090 cites W1999291377 @default.
- W3150874090 cites W2002419702 @default.
- W3150874090 cites W2015330329 @default.
- W3150874090 cites W2031402162 @default.
- W3150874090 cites W2053154970 @default.
- W3150874090 cites W2062351244 @default.
- W3150874090 cites W2064733273 @default.
- W3150874090 cites W2068299698 @default.
- W3150874090 cites W2071485689 @default.
- W3150874090 cites W2080935291 @default.
- W3150874090 cites W2085255923 @default.
- W3150874090 cites W2098824882 @default.
- W3150874090 cites W2110680246 @default.
- W3150874090 cites W2111006060 @default.
- W3150874090 cites W2114751881 @default.
- W3150874090 cites W2124357902 @default.
- W3150874090 cites W2125283600 @default.
- W3150874090 cites W2146496044 @default.
- W3150874090 cites W2152189404 @default.
- W3150874090 cites W2153161782 @default.
- W3150874090 cites W2167917621 @default.
- W3150874090 cites W2168558929 @default.
- W3150874090 cites W2171429758 @default.
- W3150874090 cites W2193407196 @default.
- W3150874090 cites W2201427885 @default.
- W3150874090 cites W2294798173 @default.
- W3150874090 cites W2488793338 @default.
- W3150874090 cites W2615933803 @default.
- W3150874090 cites W2621194466 @default.
- W3150874090 cites W2751267189 @default.
- W3150874090 cites W2763033131 @default.
- W3150874090 cites W2768753204 @default.
- W3150874090 cites W2773235741 @default.
- W3150874090 cites W2789468197 @default.
- W3150874090 cites W2790001494 @default.
- W3150874090 cites W2790364400 @default.
- W3150874090 cites W2791400636 @default.
- W3150874090 cites W2795010924 @default.
- W3150874090 cites W2795391246 @default.
- W3150874090 cites W2795411881 @default.
- W3150874090 cites W2799753289 @default.
- W3150874090 cites W2831439818 @default.
- W3150874090 cites W2911964244 @default.
- W3150874090 cites W2920083100 @default.
- W3150874090 cites W2969489994 @default.
- W3150874090 cites W2974746375 @default.
- W3150874090 cites W2985465611 @default.
- W3150874090 cites W3012173658 @default.
- W3150874090 cites W3036995291 @default.
- W3150874090 cites W3047993379 @default.
- W3150874090 cites W3109557051 @default.
- W3150874090 doi "https://doi.org/10.1016/j.ress.2021.107614" @default.
- W3150874090 hasPublicationYear "2021" @default.
- W3150874090 type Work @default.
- W3150874090 sameAs 3150874090 @default.
- W3150874090 citedByCount "6" @default.
- W3150874090 countsByYear W31508740902021 @default.
- W3150874090 countsByYear W31508740902022 @default.
- W3150874090 countsByYear W31508740902023 @default.
- W3150874090 crossrefType "journal-article" @default.
- W3150874090 hasAuthorship W3150874090A5002277423 @default.
- W3150874090 hasAuthorship W3150874090A5013942581 @default.
- W3150874090 hasAuthorship W3150874090A5019302005 @default.
- W3150874090 hasAuthorship W3150874090A5043514538 @default.
- W3150874090 hasAuthorship W3150874090A5049141644 @default.
- W3150874090 hasAuthorship W3150874090A5052025135 @default.
- W3150874090 hasAuthorship W3150874090A5055704117 @default.
- W3150874090 hasAuthorship W3150874090A5057872133 @default.
- W3150874090 hasAuthorship W3150874090A5061595221 @default.
- W3150874090 hasAuthorship W3150874090A5068109451 @default.
- W3150874090 hasAuthorship W3150874090A5081219742 @default.
- W3150874090 hasConcept C119857082 @default.
- W3150874090 hasConcept C121332964 @default.
- W3150874090 hasConcept C124101348 @default.
- W3150874090 hasConcept C127313418 @default.
- W3150874090 hasConcept C127413603 @default.
- W3150874090 hasConcept C152745839 @default.
- W3150874090 hasConcept C154945302 @default.
- W3150874090 hasConcept C163258240 @default.
- W3150874090 hasConcept C165205528 @default.
- W3150874090 hasConcept C169258074 @default.
- W3150874090 hasConcept C172707124 @default.