FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2785144003> ?p ?o ?g. }

• W2785144003 endingPage "109" @default.
• W2785144003 startingPage "109" @default.
• W2785144003 abstract "In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size." @default.
• W2785144003 created "2018-02-02" @default.
• W2785144003 creator A5018309138 @default.
• W2785144003 creator A5059572681 @default.
• W2785144003 date "2018-01-02" @default.
• W2785144003 modified "2023-10-01" @default.
• W2785144003 title "Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation" @default.
• W2785144003 cites W1964499441 @default.
• W2785144003 cites W1990996985 @default.
• W2785144003 cites W2006816577 @default.
• W2785144003 cites W2023372398 @default.
• W2785144003 cites W2048014810 @default.
• W2785144003 cites W2124991176 @default.
• W2785144003 cites W2139356817 @default.
• W2785144003 cites W2189709317 @default.
• W2785144003 cites W2508309352 @default.
• W2785144003 cites W2740483898 @default.
• W2785144003 cites W2751105615 @default.
• W2785144003 doi "https://doi.org/10.3390/s18010109" @default.
• W2785144003 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5795951" @default.
• W2785144003 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29301294" @default.
• W2785144003 hasPublicationYear "2018" @default.
• W2785144003 type Work @default.
• W2785144003 sameAs 2785144003 @default.
• W2785144003 citedByCount "48" @default.
• W2785144003 countsByYear W27851440032018 @default.
• W2785144003 countsByYear W27851440032019 @default.
• W2785144003 countsByYear W27851440032020 @default.
• W2785144003 countsByYear W27851440032021 @default.
• W2785144003 countsByYear W27851440032022 @default.
• W2785144003 countsByYear W27851440032023 @default.
• W2785144003 crossrefType "journal-article" @default.
• W2785144003 hasAuthorship W2785144003A5018309138 @default.
• W2785144003 hasAuthorship W2785144003A5059572681 @default.
• W2785144003 hasBestOaLocation W27851440031 @default.
• W2785144003 hasConcept C107637996 @default.
• W2785144003 hasConcept C115961682 @default.
• W2785144003 hasConcept C121332964 @default.
• W2785144003 hasConcept C127413603 @default.
• W2785144003 hasConcept C139719470 @default.
• W2785144003 hasConcept C153180895 @default.
• W2785144003 hasConcept C154945302 @default.
• W2785144003 hasConcept C162324750 @default.
• W2785144003 hasConcept C16389437 @default.
• W2785144003 hasConcept C199360897 @default.
• W2785144003 hasConcept C20892748 @default.
• W2785144003 hasConcept C24890656 @default.
• W2785144003 hasConcept C2777042071 @default.
• W2785144003 hasConcept C2779843651 @default.
• W2785144003 hasConcept C2781373598 @default.
• W2785144003 hasConcept C41008148 @default.
• W2785144003 hasConcept C50644808 @default.
• W2785144003 hasConcept C56529433 @default.
• W2785144003 hasConcept C62520636 @default.
• W2785144003 hasConcept C66938386 @default.
• W2785144003 hasConcept C78519656 @default.
• W2785144003 hasConcept C99498987 @default.
• W2785144003 hasConceptScore W2785144003C107637996 @default.
• W2785144003 hasConceptScore W2785144003C115961682 @default.
• W2785144003 hasConceptScore W2785144003C121332964 @default.
• W2785144003 hasConceptScore W2785144003C127413603 @default.
• W2785144003 hasConceptScore W2785144003C139719470 @default.
• W2785144003 hasConceptScore W2785144003C153180895 @default.
• W2785144003 hasConceptScore W2785144003C154945302 @default.
• W2785144003 hasConceptScore W2785144003C162324750 @default.
• W2785144003 hasConceptScore W2785144003C16389437 @default.
• W2785144003 hasConceptScore W2785144003C199360897 @default.
• W2785144003 hasConceptScore W2785144003C20892748 @default.
• W2785144003 hasConceptScore W2785144003C24890656 @default.
• W2785144003 hasConceptScore W2785144003C2777042071 @default.
• W2785144003 hasConceptScore W2785144003C2779843651 @default.
• W2785144003 hasConceptScore W2785144003C2781373598 @default.
• W2785144003 hasConceptScore W2785144003C41008148 @default.
• W2785144003 hasConceptScore W2785144003C50644808 @default.
• W2785144003 hasConceptScore W2785144003C56529433 @default.
• W2785144003 hasConceptScore W2785144003C62520636 @default.
• W2785144003 hasConceptScore W2785144003C66938386 @default.
• W2785144003 hasConceptScore W2785144003C78519656 @default.
• W2785144003 hasConceptScore W2785144003C99498987 @default.
• W2785144003 hasIssue "1" @default.
• W2785144003 hasLocation W27851440031 @default.
• W2785144003 hasLocation W27851440032 @default.
• W2785144003 hasLocation W27851440033 @default.
• W2785144003 hasLocation W27851440034 @default.
• W2785144003 hasOpenAccess W2785144003 @default.
• W2785144003 hasPrimaryLocation W27851440031 @default.
• W2785144003 hasRelatedWork W2012322462 @default.
• W2785144003 hasRelatedWork W2055028692 @default.
• W2785144003 hasRelatedWork W2101676717 @default.
• W2785144003 hasRelatedWork W2352406638 @default.
• W2785144003 hasRelatedWork W2366140178 @default.
• W2785144003 hasRelatedWork W2386821789 @default.
• W2785144003 hasRelatedWork W2393527582 @default.
• W2785144003 hasRelatedWork W2911129403 @default.
• W2785144003 hasRelatedWork W3024487667 @default.
• W2785144003 hasRelatedWork W4312401728 @default.
• W2785144003 hasVolume "18" @default.
• W2785144003 isParatext "false" @default.

• next