Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208271024> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3208271024 endingPage "10604" @default.
- W3208271024 startingPage "10595" @default.
- W3208271024 abstract "Due to its ability to learn the relationship among nodes from graph data, the graph convolution network (GCN) has received extensive attention. In the machine fault diagnosis field, it needs to construct input graphs reflecting features and relationships of the monitoring signals. Thus, the quality of the input graph affects the diagnostic performance. But it still has two limitations: 1) the constructed input graph usually has redundant edges, consuming excessive computational costs; 2) the constructed input graph cannot reflect the relationship between the noisy signals well. In order to overcome them, a dynamic graph-based feature learning with few edges considering noisy samples is proposed for rotating machinery fault diagnosis in this article. Noisy vibration signals are converted into one spectrum feature-based static graph, where redundant edges are simplified by the distance metric function. Edge connections of the input static graph are updated according to the relationship among high-level features extracted by the GCN. Based on this, dynamic input graphs are reconstructed as new graph representations for noisy samples. To verify the effectiveness of the proposed method, validation experiments were conducted on practical platforms, and results show that the dynamic input graph with few edges can effectively improve the diagnostic performance under different SNRs." @default.
- W3208271024 created "2021-11-08" @default.
- W3208271024 creator A5015773896 @default.
- W3208271024 creator A5017503188 @default.
- W3208271024 creator A5049728572 @default.
- W3208271024 creator A5085352453 @default.
- W3208271024 date "2022-10-01" @default.
- W3208271024 modified "2023-10-16" @default.
- W3208271024 title "Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis" @default.
- W3208271024 cites W1965444439 @default.
- W3208271024 cites W2007030286 @default.
- W3208271024 cites W2071908367 @default.
- W3208271024 cites W2101562895 @default.
- W3208271024 cites W2344037786 @default.
- W3208271024 cites W2405220257 @default.
- W3208271024 cites W2566499772 @default.
- W3208271024 cites W2581887665 @default.
- W3208271024 cites W2692693673 @default.
- W3208271024 cites W2787225914 @default.
- W3208271024 cites W2792461833 @default.
- W3208271024 cites W2800911105 @default.
- W3208271024 cites W2802686655 @default.
- W3208271024 cites W2887782657 @default.
- W3208271024 cites W2889185022 @default.
- W3208271024 cites W2911600220 @default.
- W3208271024 cites W2922660557 @default.
- W3208271024 cites W2943389092 @default.
- W3208271024 cites W2952852625 @default.
- W3208271024 cites W2962946486 @default.
- W3208271024 cites W2969690716 @default.
- W3208271024 cites W2988622000 @default.
- W3208271024 cites W2994835796 @default.
- W3208271024 cites W2998970859 @default.
- W3208271024 cites W2999301586 @default.
- W3208271024 cites W3005486352 @default.
- W3208271024 cites W3008309516 @default.
- W3208271024 cites W3015173390 @default.
- W3208271024 cites W3039216919 @default.
- W3208271024 cites W3040060312 @default.
- W3208271024 cites W3045200674 @default.
- W3208271024 cites W3108847889 @default.
- W3208271024 cites W3110510730 @default.
- W3208271024 cites W3119853956 @default.
- W3208271024 cites W3119945407 @default.
- W3208271024 cites W3121022549 @default.
- W3208271024 cites W3126338370 @default.
- W3208271024 cites W3157039246 @default.
- W3208271024 cites W3178034484 @default.
- W3208271024 doi "https://doi.org/10.1109/tie.2021.3121748" @default.
- W3208271024 hasPublicationYear "2022" @default.
- W3208271024 type Work @default.
- W3208271024 sameAs 3208271024 @default.
- W3208271024 citedByCount "37" @default.
- W3208271024 countsByYear W32082710242022 @default.
- W3208271024 countsByYear W32082710242023 @default.
- W3208271024 crossrefType "journal-article" @default.
- W3208271024 hasAuthorship W3208271024A5015773896 @default.
- W3208271024 hasAuthorship W3208271024A5017503188 @default.
- W3208271024 hasAuthorship W3208271024A5049728572 @default.
- W3208271024 hasAuthorship W3208271024A5085352453 @default.
- W3208271024 hasConcept C11413529 @default.
- W3208271024 hasConcept C132525143 @default.
- W3208271024 hasConcept C153180895 @default.
- W3208271024 hasConcept C154945302 @default.
- W3208271024 hasConcept C41008148 @default.
- W3208271024 hasConcept C80444323 @default.
- W3208271024 hasConceptScore W3208271024C11413529 @default.
- W3208271024 hasConceptScore W3208271024C132525143 @default.
- W3208271024 hasConceptScore W3208271024C153180895 @default.
- W3208271024 hasConceptScore W3208271024C154945302 @default.
- W3208271024 hasConceptScore W3208271024C41008148 @default.
- W3208271024 hasConceptScore W3208271024C80444323 @default.
- W3208271024 hasIssue "10" @default.
- W3208271024 hasLocation W32082710241 @default.
- W3208271024 hasOpenAccess W3208271024 @default.
- W3208271024 hasPrimaryLocation W32082710241 @default.
- W3208271024 hasRelatedWork W1978450727 @default.
- W3208271024 hasRelatedWork W2033914206 @default.
- W3208271024 hasRelatedWork W2146076056 @default.
- W3208271024 hasRelatedWork W2163831990 @default.
- W3208271024 hasRelatedWork W2378160586 @default.
- W3208271024 hasRelatedWork W2996038082 @default.
- W3208271024 hasRelatedWork W3003836766 @default.
- W3208271024 hasRelatedWork W3107474891 @default.
- W3208271024 hasRelatedWork W3184582087 @default.
- W3208271024 hasRelatedWork W4244943737 @default.
- W3208271024 hasVolume "69" @default.
- W3208271024 isParatext "false" @default.
- W3208271024 isRetracted "false" @default.
- W3208271024 magId "3208271024" @default.
- W3208271024 workType "article" @default.