Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381741480> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W4381741480 endingPage "7424" @default.
- W4381741480 startingPage "7424" @default.
- W4381741480 abstract "As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency." @default.
- W4381741480 created "2023-06-24" @default.
- W4381741480 creator A5010245958 @default.
- W4381741480 creator A5016499421 @default.
- W4381741480 creator A5044950437 @default.
- W4381741480 creator A5066050260 @default.
- W4381741480 creator A5071187277 @default.
- W4381741480 date "2023-06-22" @default.
- W4381741480 modified "2023-10-14" @default.
- W4381741480 title "Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division" @default.
- W4381741480 cites W2019505419 @default.
- W4381741480 cites W2097117768 @default.
- W4381741480 cites W2097749765 @default.
- W4381741480 cites W2100495367 @default.
- W4381741480 cites W2157331557 @default.
- W4381741480 cites W2255466643 @default.
- W4381741480 cites W243674440 @default.
- W4381741480 cites W2618530766 @default.
- W4381741480 cites W2904460913 @default.
- W4381741480 cites W2920611841 @default.
- W4381741480 cites W2966008650 @default.
- W4381741480 cites W2972641997 @default.
- W4381741480 cites W2981905076 @default.
- W4381741480 cites W2985380938 @default.
- W4381741480 cites W2989638106 @default.
- W4381741480 cites W2999342951 @default.
- W4381741480 cites W3037944824 @default.
- W4381741480 cites W3150298593 @default.
- W4381741480 cites W3210628769 @default.
- W4381741480 cites W3215145458 @default.
- W4381741480 cites W3217566380 @default.
- W4381741480 cites W4200435622 @default.
- W4381741480 cites W4213415533 @default.
- W4381741480 cites W4221077755 @default.
- W4381741480 cites W4224935930 @default.
- W4381741480 cites W4283592273 @default.
- W4381741480 cites W4296219852 @default.
- W4381741480 cites W4306722552 @default.
- W4381741480 cites W4308766051 @default.
- W4381741480 cites W4321615196 @default.
- W4381741480 doi "https://doi.org/10.3390/app13137424" @default.
- W4381741480 hasPublicationYear "2023" @default.
- W4381741480 type Work @default.
- W4381741480 citedByCount "1" @default.
- W4381741480 countsByYear W43817414802023 @default.
- W4381741480 crossrefType "journal-article" @default.
- W4381741480 hasAuthorship W4381741480A5010245958 @default.
- W4381741480 hasAuthorship W4381741480A5016499421 @default.
- W4381741480 hasAuthorship W4381741480A5044950437 @default.
- W4381741480 hasAuthorship W4381741480A5066050260 @default.
- W4381741480 hasAuthorship W4381741480A5071187277 @default.
- W4381741480 hasBestOaLocation W43817414801 @default.
- W4381741480 hasConcept C108583219 @default.
- W4381741480 hasConcept C119857082 @default.
- W4381741480 hasConcept C127313418 @default.
- W4381741480 hasConcept C153180895 @default.
- W4381741480 hasConcept C154945302 @default.
- W4381741480 hasConcept C165205528 @default.
- W4381741480 hasConcept C175551986 @default.
- W4381741480 hasConcept C199978012 @default.
- W4381741480 hasConcept C41008148 @default.
- W4381741480 hasConcept C81363708 @default.
- W4381741480 hasConceptScore W4381741480C108583219 @default.
- W4381741480 hasConceptScore W4381741480C119857082 @default.
- W4381741480 hasConceptScore W4381741480C127313418 @default.
- W4381741480 hasConceptScore W4381741480C153180895 @default.
- W4381741480 hasConceptScore W4381741480C154945302 @default.
- W4381741480 hasConceptScore W4381741480C165205528 @default.
- W4381741480 hasConceptScore W4381741480C175551986 @default.
- W4381741480 hasConceptScore W4381741480C199978012 @default.
- W4381741480 hasConceptScore W4381741480C41008148 @default.
- W4381741480 hasConceptScore W4381741480C81363708 @default.
- W4381741480 hasIssue "13" @default.
- W4381741480 hasLocation W43817414801 @default.
- W4381741480 hasOpenAccess W4381741480 @default.
- W4381741480 hasPrimaryLocation W43817414801 @default.
- W4381741480 hasRelatedWork W2731899572 @default.
- W4381741480 hasRelatedWork W2999805992 @default.
- W4381741480 hasRelatedWork W3116150086 @default.
- W4381741480 hasRelatedWork W3133861977 @default.
- W4381741480 hasRelatedWork W4200173597 @default.
- W4381741480 hasRelatedWork W4223943233 @default.
- W4381741480 hasRelatedWork W4291897433 @default.
- W4381741480 hasRelatedWork W4312417841 @default.
- W4381741480 hasRelatedWork W4321369474 @default.
- W4381741480 hasRelatedWork W4380075502 @default.
- W4381741480 hasVolume "13" @default.
- W4381741480 isParatext "false" @default.
- W4381741480 isRetracted "false" @default.
- W4381741480 workType "article" @default.