FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313372167> ?p ?o ?g. }

• W4313372167 endingPage "4285" @default.
• W4313372167 startingPage "4263" @default.
• W4313372167 abstract "Abstract The condition of bearings has a significant impact on the healthy operation of mechanical equipment, which leads to a tremendous attention on fault diagnosis algorithms. However, due to the complex working environment and severe noise interference, training a robust bearing fault diagnosis model is considered to be a difficult task. To address this problem, a multiscale frequency division denoising network (MFDDN) model is proposed, where the frequency division denoising modules are presented to extract the detail fault features, and multiscale convolution neural network is employed to learn and enrich the overall fault features through two-scale convolution channels communication. The stacking convolution pooling layers are adopted to deepen the large-scale convolution channel and learn abundant global features. To remove the noise in the small-scale convolution channel, the frequency division denoising layers are constructed based on wavelet analysis to acquire the features of noise, where the input feature map is separated into high frequency and low-frequency features, and a sub-network based on attention mechanism is established for adaptive denoising. The superior features of MFDDN are the fusion of important fault features at each scale and custom learning of fine-grained features for the adaptive denoising, which improves the network feature extraction capability and noise robustness. This paper compares the performance of MFDDN with several common bearing fault diagnosis models on two benchmark bearing fault datasets. Extensive experiments show the state-of-the-art performance including robustness, generalization, and accuracy compared to the other methods under complex noise environment." @default.
• W4313372167 created "2023-01-06" @default.
• W4313372167 creator A5069948088 @default.
• W4313372167 creator A5088430012 @default.
• W4313372167 date "2022-12-30" @default.
• W4313372167 modified "2023-09-25" @default.
• W4313372167 title "A multiscale convolution neural network for bearing fault diagnosis based on frequency division denoising under complex noise conditions" @default.
• W4313372167 cites W1973176617 @default.
• W4313372167 cites W1992397769 @default.
• W4313372167 cites W1993785147 @default.
• W4313372167 cites W2000911430 @default.
• W4313372167 cites W2006523447 @default.
• W4313372167 cites W2045612479 @default.
• W4313372167 cites W2051483544 @default.
• W4313372167 cites W2057577134 @default.
• W4313372167 cites W2066327624 @default.
• W4313372167 cites W2081102568 @default.
• W4313372167 cites W2092460715 @default.
• W4313372167 cites W2128734408 @default.
• W4313372167 cites W2189090994 @default.
• W4313372167 cites W2343409267 @default.
• W4313372167 cites W2741636173 @default.
• W4313372167 cites W2744790985 @default.
• W4313372167 cites W2758113345 @default.
• W4313372167 cites W2778801251 @default.
• W4313372167 cites W2892075914 @default.
• W4313372167 cites W2893747136 @default.
• W4313372167 cites W2903580591 @default.
• W4313372167 cites W2956927451 @default.
• W4313372167 cites W2963420686 @default.
• W4313372167 cites W2977117446 @default.
• W4313372167 cites W2979655715 @default.
• W4313372167 cites W2981850489 @default.
• W4313372167 cites W2988396473 @default.
• W4313372167 cites W3009370740 @default.
• W4313372167 cites W3013328650 @default.
• W4313372167 cites W3019762726 @default.
• W4313372167 cites W3022386145 @default.
• W4313372167 cites W3025926773 @default.
• W4313372167 cites W3045844893 @default.
• W4313372167 cites W3097068663 @default.
• W4313372167 cites W3120741450 @default.
• W4313372167 cites W3137171669 @default.
• W4313372167 cites W3147206993 @default.
• W4313372167 cites W3160217442 @default.
• W4313372167 cites W3191184807 @default.
• W4313372167 cites W4200221661 @default.
• W4313372167 cites W4281791607 @default.
• W4313372167 doi "https://doi.org/10.1007/s40747-022-00925-0" @default.
• W4313372167 hasPublicationYear "2022" @default.
• W4313372167 type Work @default.
• W4313372167 citedByCount "0" @default.
• W4313372167 crossrefType "journal-article" @default.
• W4313372167 hasAuthorship W4313372167A5069948088 @default.
• W4313372167 hasAuthorship W4313372167A5088430012 @default.
• W4313372167 hasBestOaLocation W43133721671 @default.
• W4313372167 hasConcept C104317684 @default.
• W4313372167 hasConcept C115961682 @default.
• W4313372167 hasConcept C127313418 @default.
• W4313372167 hasConcept C153180895 @default.
• W4313372167 hasConcept C154945302 @default.
• W4313372167 hasConcept C163294075 @default.
• W4313372167 hasConcept C165205528 @default.
• W4313372167 hasConcept C175551986 @default.
• W4313372167 hasConcept C185592680 @default.
• W4313372167 hasConcept C41008148 @default.
• W4313372167 hasConcept C47432892 @default.
• W4313372167 hasConcept C50644808 @default.
• W4313372167 hasConcept C55493867 @default.
• W4313372167 hasConcept C63479239 @default.
• W4313372167 hasConcept C81363708 @default.
• W4313372167 hasConcept C99498987 @default.
• W4313372167 hasConceptScore W4313372167C104317684 @default.
• W4313372167 hasConceptScore W4313372167C115961682 @default.
• W4313372167 hasConceptScore W4313372167C127313418 @default.
• W4313372167 hasConceptScore W4313372167C153180895 @default.
• W4313372167 hasConceptScore W4313372167C154945302 @default.
• W4313372167 hasConceptScore W4313372167C163294075 @default.
• W4313372167 hasConceptScore W4313372167C165205528 @default.
• W4313372167 hasConceptScore W4313372167C175551986 @default.
• W4313372167 hasConceptScore W4313372167C185592680 @default.
• W4313372167 hasConceptScore W4313372167C41008148 @default.
• W4313372167 hasConceptScore W4313372167C47432892 @default.
• W4313372167 hasConceptScore W4313372167C50644808 @default.
• W4313372167 hasConceptScore W4313372167C55493867 @default.
• W4313372167 hasConceptScore W4313372167C63479239 @default.
• W4313372167 hasConceptScore W4313372167C81363708 @default.
• W4313372167 hasConceptScore W4313372167C99498987 @default.
• W4313372167 hasFunder F4320321001 @default.
• W4313372167 hasFunder F4320336350 @default.
• W4313372167 hasIssue "4" @default.
• W4313372167 hasLocation W43133721671 @default.
• W4313372167 hasOpenAccess W4313372167 @default.
• W4313372167 hasPrimaryLocation W43133721671 @default.
• W4313372167 hasRelatedWork W1555440674 @default.
• W4313372167 hasRelatedWork W2541950815 @default.
• W4313372167 hasRelatedWork W2767651786 @default.
• W4313372167 hasRelatedWork W2912288872 @default.

• next