FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2904172802> ?p ?o ?g. }

• W2904172802 endingPage "168781401881718" @default.
• W2904172802 startingPage "168781401881718" @default.
• W2904172802 abstract "For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability." @default.
• W2904172802 created "2018-12-22" @default.
• W2904172802 creator A5020496314 @default.
• W2904172802 creator A5033586311 @default.
• W2904172802 creator A5081192942 @default.
• W2904172802 creator A5089254601 @default.
• W2904172802 date "2018-12-01" @default.
• W2904172802 modified "2023-10-16" @default.
• W2904172802 title "Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network" @default.
• W2904172802 cites W2007221293 @default.
• W2904172802 cites W2020623375 @default.
• W2904172802 cites W2021438544 @default.
• W2904172802 cites W2022141820 @default.
• W2904172802 cites W2030166992 @default.
• W2904172802 cites W2043779128 @default.
• W2904172802 cites W2053443947 @default.
• W2904172802 cites W2064675550 @default.
• W2904172802 cites W2093190672 @default.
• W2904172802 cites W2101976214 @default.
• W2904172802 cites W2147800946 @default.
• W2904172802 cites W2152074354 @default.
• W2904172802 cites W2219903032 @default.
• W2904172802 cites W2485614840 @default.
• W2904172802 cites W2580840020 @default.
• W2904172802 cites W2582662555 @default.
• W2904172802 cites W2591055632 @default.
• W2904172802 cites W2592062672 @default.
• W2904172802 cites W2601590138 @default.
• W2904172802 cites W2604957411 @default.
• W2904172802 cites W2617137613 @default.
• W2904172802 cites W2746111230 @default.
• W2904172802 cites W2773549135 @default.
• W2904172802 cites W2777200870 @default.
• W2904172802 cites W2792018332 @default.
• W2904172802 cites W2793039588 @default.
• W2904172802 cites W2796628542 @default.
• W2904172802 cites W2799598492 @default.
• W2904172802 cites W2802866110 @default.
• W2904172802 doi "https://doi.org/10.1177/1687814018817184" @default.
• W2904172802 hasPublicationYear "2018" @default.
• W2904172802 type Work @default.
• W2904172802 sameAs 2904172802 @default.
• W2904172802 citedByCount "67" @default.
• W2904172802 countsByYear W29041728022019 @default.
• W2904172802 countsByYear W29041728022020 @default.
• W2904172802 countsByYear W29041728022021 @default.
• W2904172802 countsByYear W29041728022022 @default.
• W2904172802 countsByYear W29041728022023 @default.
• W2904172802 crossrefType "journal-article" @default.
• W2904172802 hasAuthorship W2904172802A5020496314 @default.
• W2904172802 hasAuthorship W2904172802A5033586311 @default.
• W2904172802 hasAuthorship W2904172802A5081192942 @default.
• W2904172802 hasAuthorship W2904172802A5089254601 @default.
• W2904172802 hasBestOaLocation W29041728021 @default.
• W2904172802 hasConcept C108583219 @default.
• W2904172802 hasConcept C119857082 @default.
• W2904172802 hasConcept C127313418 @default.
• W2904172802 hasConcept C138885662 @default.
• W2904172802 hasConcept C153180895 @default.
• W2904172802 hasConcept C154945302 @default.
• W2904172802 hasConcept C165205528 @default.
• W2904172802 hasConcept C175551986 @default.
• W2904172802 hasConcept C17744445 @default.
• W2904172802 hasConcept C199539241 @default.
• W2904172802 hasConcept C199978012 @default.
• W2904172802 hasConcept C2776359362 @default.
• W2904172802 hasConcept C2776401178 @default.
• W2904172802 hasConcept C41008148 @default.
• W2904172802 hasConcept C41895202 @default.
• W2904172802 hasConcept C50644808 @default.
• W2904172802 hasConcept C52622490 @default.
• W2904172802 hasConcept C59404180 @default.
• W2904172802 hasConcept C81363708 @default.
• W2904172802 hasConcept C94625758 @default.
• W2904172802 hasConcept C97385483 @default.
• W2904172802 hasConcept C97931131 @default.
• W2904172802 hasConceptScore W2904172802C108583219 @default.
• W2904172802 hasConceptScore W2904172802C119857082 @default.
• W2904172802 hasConceptScore W2904172802C127313418 @default.
• W2904172802 hasConceptScore W2904172802C138885662 @default.
• W2904172802 hasConceptScore W2904172802C153180895 @default.
• W2904172802 hasConceptScore W2904172802C154945302 @default.
• W2904172802 hasConceptScore W2904172802C165205528 @default.
• W2904172802 hasConceptScore W2904172802C175551986 @default.
• W2904172802 hasConceptScore W2904172802C17744445 @default.
• W2904172802 hasConceptScore W2904172802C199539241 @default.
• W2904172802 hasConceptScore W2904172802C199978012 @default.
• W2904172802 hasConceptScore W2904172802C2776359362 @default.
• W2904172802 hasConceptScore W2904172802C2776401178 @default.
• W2904172802 hasConceptScore W2904172802C41008148 @default.
• W2904172802 hasConceptScore W2904172802C41895202 @default.
• W2904172802 hasConceptScore W2904172802C50644808 @default.
• W2904172802 hasConceptScore W2904172802C52622490 @default.
• W2904172802 hasConceptScore W2904172802C59404180 @default.
• W2904172802 hasConceptScore W2904172802C81363708 @default.
• W2904172802 hasConceptScore W2904172802C94625758 @default.
• W2904172802 hasConceptScore W2904172802C97385483 @default.
• W2904172802 hasConceptScore W2904172802C97931131 @default.

• next