FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386586216> ?p ?o ?g. }

• W4386586216 endingPage "107117" @default.
• W4386586216 startingPage "107117" @default.
• W4386586216 abstract "Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee safety and reliability of mechanical systems. Due to the variability of machinery working condition and difficulty of data obtaining under different health states, it is desirable to enhance the generalization capability to unseen working conditions for the fault diagnosis models trained by available data sets under limited number of working conditions. Considering that labeling industrial data is also a laborious work, this paper proposes a novel semi-supervised domain generalization model, termed domain-invariant feature fusion networks (DIFFN) for intelligent fault diagnosis under unseen target working conditions. The main contributions are that, intra-domain-invariant features are considered to capture the intrinsic semantic information within the domain and are fused with inter-domain-invariant features to enhance the discrimination and generalization abilities in fault diagnosis. First, a domain-invariant representation learning method is established to learn the inter- and intra-domain-invariant features using two network branches and fuse them via a fusion module. Second, a mutual learning strategy is designed to enable the network branches and the fusion module to learn from each other, thereby improving the discrimination of the extracted features for accurate fault diagnosis. Lastly, a feature divergence maximization strategy is embedded between the two network branches to improve the generalization ability of the fault diagnosis model. Experiments on two bearing data sets demonstrate that the proposed model has better diagnostic accuracy and stability over state-of-the-art semi-supervised domain generalization methods, indicating its great potential for application in generalization fault diagnosis of machinery under unseen target working conditions." @default.
• W4386586216 created "2023-09-11" @default.
• W4386586216 creator A5008653906 @default.
• W4386586216 creator A5054789987 @default.
• W4386586216 creator A5059036742 @default.
• W4386586216 creator A5072145928 @default.
• W4386586216 date "2023-11-01" @default.
• W4386586216 modified "2023-10-14" @default.
• W4386586216 title "Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis" @default.
• W4386586216 cites W2734669076 @default.
• W4386586216 cites W2892709813 @default.
• W4386586216 cites W2898760173 @default.
• W4386586216 cites W2914298094 @default.
• W4386586216 cites W2974602982 @default.
• W4386586216 cites W2976201301 @default.
• W4386586216 cites W2987480074 @default.
• W4386586216 cites W2993397516 @default.
• W4386586216 cites W3020886907 @default.
• W4386586216 cites W3046248607 @default.
• W4386586216 cites W3048813124 @default.
• W4386586216 cites W3119699510 @default.
• W4386586216 cites W3127617278 @default.
• W4386586216 cites W3138762281 @default.
• W4386586216 cites W3160541836 @default.
• W4386586216 cites W3167996502 @default.
• W4386586216 cites W3184551298 @default.
• W4386586216 cites W3192024931 @default.
• W4386586216 cites W4210940432 @default.
• W4386586216 cites W4213416527 @default.
• W4386586216 cites W4220864965 @default.
• W4386586216 cites W4280605518 @default.
• W4386586216 cites W4283804496 @default.
• W4386586216 cites W4285127705 @default.
• W4386586216 cites W4285237062 @default.
• W4386586216 cites W4286253308 @default.
• W4386586216 cites W4308461508 @default.
• W4386586216 cites W4311764070 @default.
• W4386586216 cites W4312846187 @default.
• W4386586216 cites W4321793748 @default.
• W4386586216 cites W4322490990 @default.
• W4386586216 cites W4361255950 @default.
• W4386586216 cites W4377102769 @default.
• W4386586216 doi "https://doi.org/10.1016/j.engappai.2023.107117" @default.
• W4386586216 hasPublicationYear "2023" @default.
• W4386586216 type Work @default.
• W4386586216 citedByCount "0" @default.
• W4386586216 crossrefType "journal-article" @default.
• W4386586216 hasAuthorship W4386586216A5008653906 @default.
• W4386586216 hasAuthorship W4386586216A5054789987 @default.
• W4386586216 hasAuthorship W4386586216A5059036742 @default.
• W4386586216 hasAuthorship W4386586216A5072145928 @default.
• W4386586216 hasConcept C119857082 @default.
• W4386586216 hasConcept C127313418 @default.
• W4386586216 hasConcept C134306372 @default.
• W4386586216 hasConcept C138885662 @default.
• W4386586216 hasConcept C153180895 @default.
• W4386586216 hasConcept C154945302 @default.
• W4386586216 hasConcept C165205528 @default.
• W4386586216 hasConcept C175551986 @default.
• W4386586216 hasConcept C177148314 @default.
• W4386586216 hasConcept C190470478 @default.
• W4386586216 hasConcept C2776401178 @default.
• W4386586216 hasConcept C33923547 @default.
• W4386586216 hasConcept C36503486 @default.
• W4386586216 hasConcept C37914503 @default.
• W4386586216 hasConcept C41008148 @default.
• W4386586216 hasConcept C41895202 @default.
• W4386586216 hasConcept C59404180 @default.
• W4386586216 hasConceptScore W4386586216C119857082 @default.
• W4386586216 hasConceptScore W4386586216C127313418 @default.
• W4386586216 hasConceptScore W4386586216C134306372 @default.
• W4386586216 hasConceptScore W4386586216C138885662 @default.
• W4386586216 hasConceptScore W4386586216C153180895 @default.
• W4386586216 hasConceptScore W4386586216C154945302 @default.
• W4386586216 hasConceptScore W4386586216C165205528 @default.
• W4386586216 hasConceptScore W4386586216C175551986 @default.
• W4386586216 hasConceptScore W4386586216C177148314 @default.
• W4386586216 hasConceptScore W4386586216C190470478 @default.
• W4386586216 hasConceptScore W4386586216C2776401178 @default.
• W4386586216 hasConceptScore W4386586216C33923547 @default.
• W4386586216 hasConceptScore W4386586216C36503486 @default.
• W4386586216 hasConceptScore W4386586216C37914503 @default.
• W4386586216 hasConceptScore W4386586216C41008148 @default.
• W4386586216 hasConceptScore W4386586216C41895202 @default.
• W4386586216 hasConceptScore W4386586216C59404180 @default.
• W4386586216 hasFunder F4320321001 @default.
• W4386586216 hasLocation W43865862161 @default.
• W4386586216 hasOpenAccess W4386586216 @default.
• W4386586216 hasPrimaryLocation W43865862161 @default.
• W4386586216 hasRelatedWork W2025991752 @default.
• W4386586216 hasRelatedWork W2382607599 @default.
• W4386586216 hasRelatedWork W2546942002 @default.
• W4386586216 hasRelatedWork W2573334707 @default.
• W4386586216 hasRelatedWork W2592385986 @default.
• W4386586216 hasRelatedWork W2944661354 @default.
• W4386586216 hasRelatedWork W2970216048 @default.
• W4386586216 hasRelatedWork W2998168123 @default.
• W4386586216 hasRelatedWork W4287995534 @default.

• next