FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3146977894> ?p ?o ?g. }

• W3146977894 endingPage "2411" @default.
• W3146977894 startingPage "2411" @default.
• W3146977894 abstract "Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique." @default.
• W3146977894 created "2021-04-13" @default.
• W3146977894 creator A5017152974 @default.
• W3146977894 creator A5031798760 @default.
• W3146977894 creator A5038897599 @default.
• W3146977894 creator A5052200911 @default.
• W3146977894 date "2021-03-31" @default.
• W3146977894 modified "2023-10-16" @default.
• W3146977894 title "Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization" @default.
• W3146977894 cites W1999727976 @default.
• W3146977894 cites W2076063813 @default.
• W3146977894 cites W2112796928 @default.
• W3146977894 cites W2129033354 @default.
• W3146977894 cites W2151238122 @default.
• W3146977894 cites W2404692435 @default.
• W3146977894 cites W2461729787 @default.
• W3146977894 cites W2564037921 @default.
• W3146977894 cites W2584994008 @default.
• W3146977894 cites W2595657631 @default.
• W3146977894 cites W2618530766 @default.
• W3146977894 cites W2741289421 @default.
• W3146977894 cites W2744790985 @default.
• W3146977894 cites W2768753204 @default.
• W3146977894 cites W2778801251 @default.
• W3146977894 cites W2790195878 @default.
• W3146977894 cites W2791694051 @default.
• W3146977894 cites W2793062918 @default.
• W3146977894 cites W2801457104 @default.
• W3146977894 cites W2804879845 @default.
• W3146977894 cites W2895475710 @default.
• W3146977894 cites W2897250207 @default.
• W3146977894 cites W2898760173 @default.
• W3146977894 cites W2901856126 @default.
• W3146977894 cites W2954743925 @default.
• W3146977894 cites W2962949934 @default.
• W3146977894 cites W3028852288 @default.
• W3146977894 cites W3043580961 @default.
• W3146977894 cites W3081870453 @default.
• W3146977894 doi "https://doi.org/10.3390/s21072411" @default.
• W3146977894 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8036431" @default.
• W3146977894 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33807427" @default.
• W3146977894 hasPublicationYear "2021" @default.
• W3146977894 type Work @default.
• W3146977894 sameAs 3146977894 @default.
• W3146977894 citedByCount "37" @default.
• W3146977894 countsByYear W31469778942021 @default.
• W3146977894 countsByYear W31469778942022 @default.
• W3146977894 countsByYear W31469778942023 @default.
• W3146977894 crossrefType "journal-article" @default.
• W3146977894 hasAuthorship W3146977894A5017152974 @default.
• W3146977894 hasAuthorship W3146977894A5031798760 @default.
• W3146977894 hasAuthorship W3146977894A5038897599 @default.
• W3146977894 hasAuthorship W3146977894A5052200911 @default.
• W3146977894 hasBestOaLocation W31469778941 @default.
• W3146977894 hasConcept C108583219 @default.
• W3146977894 hasConcept C119857082 @default.
• W3146977894 hasConcept C127313418 @default.
• W3146977894 hasConcept C153180895 @default.
• W3146977894 hasConcept C154945302 @default.
• W3146977894 hasConcept C165205528 @default.
• W3146977894 hasConcept C175551986 @default.
• W3146977894 hasConcept C199360897 @default.
• W3146977894 hasConcept C22019652 @default.
• W3146977894 hasConcept C2778049539 @default.
• W3146977894 hasConcept C2779843651 @default.
• W3146977894 hasConcept C41008148 @default.
• W3146977894 hasConcept C50644808 @default.
• W3146977894 hasConcept C58489278 @default.
• W3146977894 hasConcept C81363708 @default.
• W3146977894 hasConcept C8642999 @default.
• W3146977894 hasConceptScore W3146977894C108583219 @default.
• W3146977894 hasConceptScore W3146977894C119857082 @default.
• W3146977894 hasConceptScore W3146977894C127313418 @default.
• W3146977894 hasConceptScore W3146977894C153180895 @default.
• W3146977894 hasConceptScore W3146977894C154945302 @default.
• W3146977894 hasConceptScore W3146977894C165205528 @default.
• W3146977894 hasConceptScore W3146977894C175551986 @default.
• W3146977894 hasConceptScore W3146977894C199360897 @default.
• W3146977894 hasConceptScore W3146977894C22019652 @default.
• W3146977894 hasConceptScore W3146977894C2778049539 @default.
• W3146977894 hasConceptScore W3146977894C2779843651 @default.
• W3146977894 hasConceptScore W3146977894C41008148 @default.
• W3146977894 hasConceptScore W3146977894C50644808 @default.
• W3146977894 hasConceptScore W3146977894C58489278 @default.
• W3146977894 hasConceptScore W3146977894C81363708 @default.
• W3146977894 hasConceptScore W3146977894C8642999 @default.
• W3146977894 hasIssue "7" @default.
• W3146977894 hasLocation W31469778941 @default.
• W3146977894 hasLocation W31469778942 @default.
• W3146977894 hasLocation W31469778943 @default.
• W3146977894 hasOpenAccess W3146977894 @default.
• W3146977894 hasPrimaryLocation W31469778941 @default.
• W3146977894 hasRelatedWork W2607442583 @default.
• W3146977894 hasRelatedWork W2967237190 @default.
• W3146977894 hasRelatedWork W2995583237 @default.
• W3146977894 hasRelatedWork W3155135229 @default.
• W3146977894 hasRelatedWork W3199199693 @default.
• W3146977894 hasRelatedWork W4206951940 @default.

• next