FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3021496598> ?p ?o ?g. }

• W3021496598 endingPage "1" @default.
• W3021496598 startingPage "1" @default.
• W3021496598 abstract "In recent years, deep learning has become a promising tool for rotary machinery fault diagnosis, but it works well only when testing samples and training samples are independent and identically distributed. In practice, rotary machinery usually works under variable speed. The change of speed leads to the variation of samples’ distribution, which can significantly decrease the performance of the deep learning model. Scholars try to utilize transfer learning techniques for solving this problem. However, most exiting methods can just work well under target speed instead of all speed, while the target samples are always required in model training. In this article, a deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed. Under the setting of semisupervised domain generalization, only one fully labeled source (LS) domain data set and one totally unlabeled source (US) domain data set are available during training. To make full use of these data, the proposed method simultaneously utilizes Wasserstein generative adversarial network with gradient penalty (WGAN-GP)-based adversarial learning and pseudolabel-based semisupervised learning for training. The transmission and bearing fault diagnosis cases are utilized for evaluation. The comparative experiments indicate that the proposed method has a better performance than other state-of-the-art methods." @default.
• W3021496598 created "2020-05-13" @default.
• W3021496598 creator A5003913508 @default.
• W3021496598 creator A5010684849 @default.
• W3021496598 creator A5036048287 @default.
• W3021496598 creator A5037156903 @default.
• W3021496598 creator A5082065343 @default.
• W3021496598 date "2020-01-01" @default.
• W3021496598 modified "2023-10-13" @default.
• W3021496598 title "Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed" @default.
• W3021496598 cites W2100495367 @default.
• W3021496598 cites W2138447111 @default.
• W3021496598 cites W2165698076 @default.
• W3021496598 cites W2210061839 @default.
• W3021496598 cites W2287972354 @default.
• W3021496598 cites W2584994008 @default.
• W3021496598 cites W2593501586 @default.
• W3021496598 cites W2609688418 @default.
• W3021496598 cites W2767031373 @default.
• W3021496598 cites W2783074568 @default.
• W3021496598 cites W2789811186 @default.
• W3021496598 cites W2791036512 @default.
• W3021496598 cites W2791694051 @default.
• W3021496598 cites W2802633978 @default.
• W3021496598 cites W2810292802 @default.
• W3021496598 cites W2887782657 @default.
• W3021496598 cites W2898375427 @default.
• W3021496598 cites W2903917280 @default.
• W3021496598 cites W2904138018 @default.
• W3021496598 cites W2904218127 @default.
• W3021496598 cites W2905166565 @default.
• W3021496598 cites W2907541186 @default.
• W3021496598 cites W2908441554 @default.
• W3021496598 cites W2942682738 @default.
• W3021496598 cites W2946048316 @default.
• W3021496598 cites W2946094116 @default.
• W3021496598 cites W2955372875 @default.
• W3021496598 cites W2968409655 @default.
• W3021496598 cites W2981982720 @default.
• W3021496598 cites W2991632793 @default.
• W3021496598 cites W2991661665 @default.
• W3021496598 cites W3005842919 @default.
• W3021496598 cites W3007050866 @default.
• W3021496598 doi "https://doi.org/10.1109/tim.2020.2992829" @default.
• W3021496598 hasPublicationYear "2020" @default.
• W3021496598 type Work @default.
• W3021496598 sameAs 3021496598 @default.
• W3021496598 citedByCount "58" @default.
• W3021496598 countsByYear W30214965982020 @default.
• W3021496598 countsByYear W30214965982021 @default.
• W3021496598 countsByYear W30214965982022 @default.
• W3021496598 countsByYear W30214965982023 @default.
• W3021496598 crossrefType "journal-article" @default.
• W3021496598 hasAuthorship W3021496598A5003913508 @default.
• W3021496598 hasAuthorship W3021496598A5010684849 @default.
• W3021496598 hasAuthorship W3021496598A5036048287 @default.
• W3021496598 hasAuthorship W3021496598A5037156903 @default.
• W3021496598 hasAuthorship W3021496598A5082065343 @default.
• W3021496598 hasConcept C103824480 @default.
• W3021496598 hasConcept C127313418 @default.
• W3021496598 hasConcept C134306372 @default.
• W3021496598 hasConcept C153180895 @default.
• W3021496598 hasConcept C154945302 @default.
• W3021496598 hasConcept C165205528 @default.
• W3021496598 hasConcept C175551986 @default.
• W3021496598 hasConcept C177148314 @default.
• W3021496598 hasConcept C182365436 @default.
• W3021496598 hasConcept C31972630 @default.
• W3021496598 hasConcept C33923547 @default.
• W3021496598 hasConcept C36503486 @default.
• W3021496598 hasConcept C41008148 @default.
• W3021496598 hasConceptScore W3021496598C103824480 @default.
• W3021496598 hasConceptScore W3021496598C127313418 @default.
• W3021496598 hasConceptScore W3021496598C134306372 @default.
• W3021496598 hasConceptScore W3021496598C153180895 @default.
• W3021496598 hasConceptScore W3021496598C154945302 @default.
• W3021496598 hasConceptScore W3021496598C165205528 @default.
• W3021496598 hasConceptScore W3021496598C175551986 @default.
• W3021496598 hasConceptScore W3021496598C177148314 @default.
• W3021496598 hasConceptScore W3021496598C182365436 @default.
• W3021496598 hasConceptScore W3021496598C31972630 @default.
• W3021496598 hasConceptScore W3021496598C33923547 @default.
• W3021496598 hasConceptScore W3021496598C36503486 @default.
• W3021496598 hasConceptScore W3021496598C41008148 @default.
• W3021496598 hasLocation W30214965981 @default.
• W3021496598 hasOpenAccess W3021496598 @default.
• W3021496598 hasPrimaryLocation W30214965981 @default.
• W3021496598 hasRelatedWork W1978450727 @default.
• W3021496598 hasRelatedWork W2033914206 @default.
• W3021496598 hasRelatedWork W2146076056 @default.
• W3021496598 hasRelatedWork W2147291813 @default.
• W3021496598 hasRelatedWork W2163831990 @default.
• W3021496598 hasRelatedWork W2378160586 @default.
• W3021496598 hasRelatedWork W2380927352 @default.
• W3021496598 hasRelatedWork W3003836766 @default.
• W3021496598 hasRelatedWork W4244943737 @default.
• W3021496598 hasRelatedWork W2289108895 @default.
• W3021496598 isParatext "false" @default.

• next