Matches in SemOpenAlex for { <https://semopenalex.org/work/W3114197253> ?p ?o ?g. }
Showing items 1 to 56 of
56
with 100 items per page.
- W3114197253 abstract "In the current machinery manufacturing industry, rotating machinery occupies a very important position. Rotating machinery mainly refers to the machinery that can complete specific functions with the help of rotary action. The main vibration faults of rotating machinery include rotor unbalance, rotor misalignment, friction between moving and static parts and looseness of support parts. Generally, large rotating machinery is generally equipped with vibration monitoring protection and fault diagnosis system to ensure the safe operation of rotating machinery, but there are some limitations. In order to find the hidden danger in the machinery in time and avoid serious mechanical accidents, this paper combines the deep learning technology in machine learning to increase the accuracy and reliability of fault identification, reduce the risk of failure, and provide more guarantee for the production safety of machinery industry. Aiming at the main faults of rotating machinery, this paper proposes a fault recognition method of rotating machinery based on LSTM. Firstly, the sample data is screened and judged effectively, and then the abnormal data are classified by LSTM and softmax. Finally, the evaluation index and result of the fault category are obtained, which is compared with other classification algorithms to show the good applicability of the algorithm. At present, there are still some problems in the model, which need to be further discussed." @default.
- W3114197253 created "2021-01-05" @default.
- W3114197253 creator A5010565829 @default.
- W3114197253 creator A5036468657 @default.
- W3114197253 creator A5049079192 @default.
- W3114197253 date "2020-10-14" @default.
- W3114197253 modified "2023-09-24" @default.
- W3114197253 title "Fault diagnosis of rotating machinery based on deep learning" @default.
- W3114197253 cites W1440202786 @default.
- W3114197253 cites W1965895201 @default.
- W3114197253 cites W1966100433 @default.
- W3114197253 cites W2121971770 @default.
- W3114197253 cites W2323403147 @default.
- W3114197253 cites W2548257861 @default.
- W3114197253 cites W2765226309 @default.
- W3114197253 cites W2802633978 @default.
- W3114197253 cites W2908441554 @default.
- W3114197253 doi "https://doi.org/10.1145/3434581.3434730" @default.
- W3114197253 hasPublicationYear "2020" @default.
- W3114197253 type Work @default.
- W3114197253 sameAs 3114197253 @default.
- W3114197253 citedByCount "2" @default.
- W3114197253 countsByYear W31141972532022 @default.
- W3114197253 crossrefType "proceedings-article" @default.
- W3114197253 hasAuthorship W3114197253A5010565829 @default.
- W3114197253 hasAuthorship W3114197253A5036468657 @default.
- W3114197253 hasAuthorship W3114197253A5049079192 @default.
- W3114197253 hasConcept C108583219 @default.
- W3114197253 hasConcept C127313418 @default.
- W3114197253 hasConcept C154945302 @default.
- W3114197253 hasConcept C165205528 @default.
- W3114197253 hasConcept C175551986 @default.
- W3114197253 hasConcept C41008148 @default.
- W3114197253 hasConceptScore W3114197253C108583219 @default.
- W3114197253 hasConceptScore W3114197253C127313418 @default.
- W3114197253 hasConceptScore W3114197253C154945302 @default.
- W3114197253 hasConceptScore W3114197253C165205528 @default.
- W3114197253 hasConceptScore W3114197253C175551986 @default.
- W3114197253 hasConceptScore W3114197253C41008148 @default.
- W3114197253 hasLocation W31141972531 @default.
- W3114197253 hasOpenAccess W3114197253 @default.
- W3114197253 hasPrimaryLocation W31141972531 @default.
- W3114197253 hasRelatedWork W2126887587 @default.
- W3114197253 hasRelatedWork W2731899572 @default.
- W3114197253 hasRelatedWork W2939353110 @default.
- W3114197253 hasRelatedWork W2941846814 @default.
- W3114197253 hasRelatedWork W2948658236 @default.
- W3114197253 hasRelatedWork W3009238340 @default.
- W3114197253 hasRelatedWork W3118091236 @default.
- W3114197253 hasRelatedWork W3215138031 @default.
- W3114197253 hasRelatedWork W4230611425 @default.
- W3114197253 hasRelatedWork W4312962853 @default.
- W3114197253 isParatext "false" @default.
- W3114197253 isRetracted "false" @default.
- W3114197253 magId "3114197253" @default.
- W3114197253 workType "article" @default.