FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200348518> ?p ?o ?g. }

• W4200348518 endingPage "192" @default.
• W4200348518 startingPage "192" @default.
• W4200348518 abstract "Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled samples are required to support the algorithm of CNNs, and, in the case of a limited amount of labeled samples, this may lead to overfitting. In this article, a novel ResNet-based method is developed to achieve fault diagnoses for machines with very few samples. To be specific, data transformation combinations (DTCs) are designed based on mutual information. It is worth noting that the selected DTC, which can complete the training process of the 1-D ResNet quickly without increasing the amount of training data, can be randomly used for any batch training data. Meanwhile, a self-supervised learning method called 1-D SimCLR is adopted to obtain an effective feature encoder, which can be optimized with very few unlabeled samples. Then, a fault diagnosis model named DTC-SimCLR is constructed by combining the selected data transformation combination, the obtained feature encoder and a fully-connected layer-based classifier. In DTC-SimCLR, the parameters of the feature encoder are fixed, and the classifier is trained with very few labeled samples. Two machine fault datasets from a cutting tooth and a bearing are conducted to evaluate the performance of DTC-SimCLR. Testing results show that DTC-SimCLR has superior performance and diagnostic accuracy with very few samples." @default.
• W4200348518 created "2021-12-31" @default.
• W4200348518 creator A5040767797 @default.
• W4200348518 creator A5055524868 @default.
• W4200348518 creator A5058132469 @default.
• W4200348518 creator A5062646617 @default.
• W4200348518 creator A5075974311 @default.
• W4200348518 date "2021-12-28" @default.
• W4200348518 modified "2023-10-11" @default.
• W4200348518 title "Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples" @default.
• W4200348518 cites W2078499342 @default.
• W4200348518 cites W2118978333 @default.
• W4200348518 cites W2137825550 @default.
• W4200348518 cites W2163984882 @default.
• W4200348518 cites W2165698076 @default.
• W4200348518 cites W2194775991 @default.
• W4200348518 cites W2399038344 @default.
• W4200348518 cites W2740570963 @default.
• W4200348518 cites W2746791238 @default.
• W4200348518 cites W2808496542 @default.
• W4200348518 cites W2810188680 @default.
• W4200348518 cites W2898138731 @default.
• W4200348518 cites W2942245950 @default.
• W4200348518 cites W2944322766 @default.
• W4200348518 cites W2951576719 @default.
• W4200348518 cites W2957277585 @default.
• W4200348518 cites W2967625104 @default.
• W4200348518 cites W2968698829 @default.
• W4200348518 cites W2989544315 @default.
• W4200348518 cites W2990851485 @default.
• W4200348518 cites W3027946011 @default.
• W4200348518 cites W3034942609 @default.
• W4200348518 cites W3039321163 @default.
• W4200348518 cites W3043896160 @default.
• W4200348518 cites W3121088397 @default.
• W4200348518 cites W3122126208 @default.
• W4200348518 cites W3126383074 @default.
• W4200348518 cites W3128871194 @default.
• W4200348518 cites W3150282857 @default.
• W4200348518 cites W3159622438 @default.
• W4200348518 cites W3165603617 @default.
• W4200348518 cites W3165802365 @default.
• W4200348518 cites W3172563942 @default.
• W4200348518 cites W3196389547 @default.
• W4200348518 doi "https://doi.org/10.3390/s22010192" @default.
• W4200348518 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35009734" @default.
• W4200348518 hasPublicationYear "2021" @default.
• W4200348518 type Work @default.
• W4200348518 citedByCount "6" @default.
• W4200348518 countsByYear W42003485182022 @default.
• W4200348518 countsByYear W42003485182023 @default.
• W4200348518 crossrefType "journal-article" @default.
• W4200348518 hasAuthorship W4200348518A5040767797 @default.
• W4200348518 hasAuthorship W4200348518A5055524868 @default.
• W4200348518 hasAuthorship W4200348518A5058132469 @default.
• W4200348518 hasAuthorship W4200348518A5062646617 @default.
• W4200348518 hasAuthorship W4200348518A5075974311 @default.
• W4200348518 hasBestOaLocation W42003485181 @default.
• W4200348518 hasConcept C111919701 @default.
• W4200348518 hasConcept C118505674 @default.
• W4200348518 hasConcept C124101348 @default.
• W4200348518 hasConcept C153180895 @default.
• W4200348518 hasConcept C154945302 @default.
• W4200348518 hasConcept C22019652 @default.
• W4200348518 hasConcept C2776145971 @default.
• W4200348518 hasConcept C41008148 @default.
• W4200348518 hasConcept C50644808 @default.
• W4200348518 hasConcept C52622490 @default.
• W4200348518 hasConcept C95623464 @default.
• W4200348518 hasConceptScore W4200348518C111919701 @default.
• W4200348518 hasConceptScore W4200348518C118505674 @default.
• W4200348518 hasConceptScore W4200348518C124101348 @default.
• W4200348518 hasConceptScore W4200348518C153180895 @default.
• W4200348518 hasConceptScore W4200348518C154945302 @default.
• W4200348518 hasConceptScore W4200348518C22019652 @default.
• W4200348518 hasConceptScore W4200348518C2776145971 @default.
• W4200348518 hasConceptScore W4200348518C41008148 @default.
• W4200348518 hasConceptScore W4200348518C50644808 @default.
• W4200348518 hasConceptScore W4200348518C52622490 @default.
• W4200348518 hasConceptScore W4200348518C95623464 @default.
• W4200348518 hasIssue "1" @default.
• W4200348518 hasLocation W42003485181 @default.
• W4200348518 hasLocation W42003485182 @default.
• W4200348518 hasLocation W42003485183 @default.
• W4200348518 hasLocation W42003485184 @default.
• W4200348518 hasOpenAccess W4200348518 @default.
• W4200348518 hasPrimaryLocation W42003485181 @default.
• W4200348518 hasRelatedWork W1964120219 @default.
• W4200348518 hasRelatedWork W2000165426 @default.
• W4200348518 hasRelatedWork W2144059113 @default.
• W4200348518 hasRelatedWork W2146076056 @default.
• W4200348518 hasRelatedWork W2385132419 @default.
• W4200348518 hasRelatedWork W2563096758 @default.
• W4200348518 hasRelatedWork W2742991909 @default.
• W4200348518 hasRelatedWork W2767651786 @default.
• W4200348518 hasRelatedWork W2811390910 @default.
• W4200348518 hasRelatedWork W3003836766 @default.
• W4200348518 hasVolume "22" @default.

• next