FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328142793> ?p ?o ?g. }

• W4328142793 endingPage "101945" @default.
• W4328142793 startingPage "101945" @default.
• W4328142793 abstract "Gearboxes are integral elements in rotating machines and have a high tendency to fail due to their operation in harsh conditions. A robust method to estimate the fault size of gears is desirable for a successful prognostic process, which is, to date, still unavailable in the literature. The fault size can be estimated by vibration analysis, using signal processing and machine-learning tools. However, the availability of labeled or unlabeled vibration signals from faulty rotating machinery components is rare, making it challenging to apply machine-learning algorithms. Therefore, some physical pre-knowledge should be incorporated in the model for a successful learning process. This can be done by exposing the learning model to simulated data, and by a physical pre-processing procedure. This paper suggests a novel algorithm to overcome the lack of faulty data (labeled and unlabeled), and it is trained on a combination of simulated data and some real data. The algorithm tunes the differences between simulation and experiment using one faulty experimental example, and transfers knowledge from simulation to reality by addressing the transfer function effects. It addresses the transfer function by spectrum background estimation and minimum phase estimation while also selecting features that are invariant to the unmitigated effects of the transfer function. The new algorithm is demonstrated on simulated signals and measured transfer function, and on experimental signals with known fault sizes. The codes and the data of the study are available via the link: https://github.com/omriMatania/one_fault_shot_learning_for_gears_fault_severity_estimation." @default.
• W4328142793 created "2023-03-22" @default.
• W4328142793 creator A5000848090 @default.
• W4328142793 creator A5011483654 @default.
• W4328142793 creator A5018003703 @default.
• W4328142793 creator A5026960827 @default.
• W4328142793 creator A5054424380 @default.
• W4328142793 creator A5070499287 @default.
• W4328142793 date "2023-04-01" @default.
• W4328142793 modified "2023-10-18" @default.
• W4328142793 title "One-fault-shot learning for fault severity estimation of gears that addresses differences between simulation and experimental signals and transfer function effects" @default.
• W4328142793 cites W1587395205 @default.
• W4328142793 cites W1970594742 @default.
• W4328142793 cites W1995439616 @default.
• W4328142793 cites W1997117443 @default.
• W4328142793 cites W2002305563 @default.
• W4328142793 cites W2002377043 @default.
• W4328142793 cites W2005297123 @default.
• W4328142793 cites W2009574183 @default.
• W4328142793 cites W2016324154 @default.
• W4328142793 cites W2019461689 @default.
• W4328142793 cites W2043281016 @default.
• W4328142793 cites W2056168656 @default.
• W4328142793 cites W2076971300 @default.
• W4328142793 cites W2077140162 @default.
• W4328142793 cites W2083102256 @default.
• W4328142793 cites W2094099988 @default.
• W4328142793 cites W2107434184 @default.
• W4328142793 cites W2117758728 @default.
• W4328142793 cites W2135032451 @default.
• W4328142793 cites W2158704884 @default.
• W4328142793 cites W2159357605 @default.
• W4328142793 cites W2167251835 @default.
• W4328142793 cites W2195459533 @default.
• W4328142793 cites W2550093246 @default.
• W4328142793 cites W2582889599 @default.
• W4328142793 cites W2604647705 @default.
• W4328142793 cites W2735326783 @default.
• W4328142793 cites W2752793022 @default.
• W4328142793 cites W2758514457 @default.
• W4328142793 cites W2767216509 @default.
• W4328142793 cites W2768768087 @default.
• W4328142793 cites W2791786163 @default.
• W4328142793 cites W2808496542 @default.
• W4328142793 cites W2811038430 @default.
• W4328142793 cites W2827159893 @default.
• W4328142793 cites W2897250207 @default.
• W4328142793 cites W2907541186 @default.
• W4328142793 cites W2913351631 @default.
• W4328142793 cites W2931233507 @default.
• W4328142793 cites W2998506103 @default.
• W4328142793 cites W3021589006 @default.
• W4328142793 cites W3027554678 @default.
• W4328142793 cites W3120767979 @default.
• W4328142793 cites W3143638720 @default.
• W4328142793 cites W3194532778 @default.
• W4328142793 cites W3196234731 @default.
• W4328142793 cites W3197829552 @default.
• W4328142793 cites W3213908205 @default.
• W4328142793 cites W4206081678 @default.
• W4328142793 cites W4212861124 @default.
• W4328142793 cites W4214838834 @default.
• W4328142793 cites W4220903119 @default.
• W4328142793 cites W4235594362 @default.
• W4328142793 cites W4236362309 @default.
• W4328142793 cites W4250160326 @default.
• W4328142793 doi "https://doi.org/10.1016/j.aei.2023.101945" @default.
• W4328142793 hasPublicationYear "2023" @default.
• W4328142793 type Work @default.
• W4328142793 citedByCount "3" @default.
• W4328142793 countsByYear W43281427932023 @default.
• W4328142793 crossrefType "journal-article" @default.
• W4328142793 hasAuthorship W4328142793A5000848090 @default.
• W4328142793 hasAuthorship W4328142793A5011483654 @default.
• W4328142793 hasAuthorship W4328142793A5018003703 @default.
• W4328142793 hasAuthorship W4328142793A5026960827 @default.
• W4328142793 hasAuthorship W4328142793A5054424380 @default.
• W4328142793 hasAuthorship W4328142793A5070499287 @default.
• W4328142793 hasConcept C111919701 @default.
• W4328142793 hasConcept C11413529 @default.
• W4328142793 hasConcept C119599485 @default.
• W4328142793 hasConcept C119857082 @default.
• W4328142793 hasConcept C121332964 @default.
• W4328142793 hasConcept C127313418 @default.
• W4328142793 hasConcept C127413603 @default.
• W4328142793 hasConcept C14036430 @default.
• W4328142793 hasConcept C150899416 @default.
• W4328142793 hasConcept C154945302 @default.
• W4328142793 hasConcept C165205528 @default.
• W4328142793 hasConcept C175551986 @default.
• W4328142793 hasConcept C198394728 @default.
• W4328142793 hasConcept C41008148 @default.
• W4328142793 hasConcept C62520636 @default.
• W4328142793 hasConcept C78458016 @default.
• W4328142793 hasConcept C81299745 @default.
• W4328142793 hasConcept C86803240 @default.
• W4328142793 hasConcept C98045186 @default.
• W4328142793 hasConceptScore W4328142793C111919701 @default.

• next