Matches in SemOpenAlex for { <https://semopenalex.org/work/W2057488242> ?p ?o ?g. }
- W2057488242 abstract "Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature." @default.
- W2057488242 created "2016-06-24" @default.
- W2057488242 creator A5015546186 @default.
- W2057488242 creator A5068256124 @default.
- W2057488242 creator A5072773240 @default.
- W2057488242 date "2014-06-20" @default.
- W2057488242 modified "2023-10-03" @default.
- W2057488242 title "Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review" @default.
- W2057488242 cites W1485993870 @default.
- W2057488242 cites W1491190894 @default.
- W2057488242 cites W1491691470 @default.
- W2057488242 cites W1556705430 @default.
- W2057488242 cites W1564485251 @default.
- W2057488242 cites W1570140017 @default.
- W2057488242 cites W187909418 @default.
- W2057488242 cites W1972243751 @default.
- W2057488242 cites W1986115104 @default.
- W2057488242 cites W1986397312 @default.
- W2057488242 cites W1993917084 @default.
- W2057488242 cites W1998927263 @default.
- W2057488242 cites W2003418909 @default.
- W2057488242 cites W2005140670 @default.
- W2057488242 cites W2016324154 @default.
- W2057488242 cites W2025109679 @default.
- W2057488242 cites W2026296412 @default.
- W2057488242 cites W2041280856 @default.
- W2057488242 cites W2048984787 @default.
- W2057488242 cites W2053788893 @default.
- W2057488242 cites W2063360974 @default.
- W2057488242 cites W2064091681 @default.
- W2057488242 cites W2071293002 @default.
- W2057488242 cites W2074003021 @default.
- W2057488242 cites W2075277519 @default.
- W2057488242 cites W2077202034 @default.
- W2057488242 cites W2082020473 @default.
- W2057488242 cites W2083226902 @default.
- W2057488242 cites W2103285028 @default.
- W2057488242 cites W2111103700 @default.
- W2057488242 cites W2125000245 @default.
- W2057488242 cites W2139212933 @default.
- W2057488242 cites W2142165357 @default.
- W2057488242 cites W2144898065 @default.
- W2057488242 cites W2146623129 @default.
- W2057488242 cites W2148603752 @default.
- W2057488242 cites W2153143252 @default.
- W2057488242 cites W2160491376 @default.
- W2057488242 cites W2171435045 @default.
- W2057488242 cites W2351507227 @default.
- W2057488242 cites W242138678 @default.
- W2057488242 cites W2604005400 @default.
- W2057488242 cites W2912565176 @default.
- W2057488242 cites W80501027 @default.
- W2057488242 doi "https://doi.org/10.11113/jt.v69.3121" @default.
- W2057488242 hasPublicationYear "2014" @default.
- W2057488242 type Work @default.
- W2057488242 sameAs 2057488242 @default.
- W2057488242 citedByCount "28" @default.
- W2057488242 countsByYear W20574882422015 @default.
- W2057488242 countsByYear W20574882422016 @default.
- W2057488242 countsByYear W20574882422018 @default.
- W2057488242 countsByYear W20574882422019 @default.
- W2057488242 countsByYear W20574882422020 @default.
- W2057488242 countsByYear W20574882422021 @default.
- W2057488242 countsByYear W20574882422022 @default.
- W2057488242 countsByYear W20574882422023 @default.
- W2057488242 crossrefType "journal-article" @default.
- W2057488242 hasAuthorship W2057488242A5015546186 @default.
- W2057488242 hasAuthorship W2057488242A5068256124 @default.
- W2057488242 hasAuthorship W2057488242A5072773240 @default.
- W2057488242 hasBestOaLocation W20574882421 @default.
- W2057488242 hasConcept C104267543 @default.
- W2057488242 hasConcept C119599485 @default.
- W2057488242 hasConcept C119857082 @default.
- W2057488242 hasConcept C121332964 @default.
- W2057488242 hasConcept C12267149 @default.
- W2057488242 hasConcept C127313418 @default.
- W2057488242 hasConcept C127413603 @default.
- W2057488242 hasConcept C152745839 @default.
- W2057488242 hasConcept C154945302 @default.
- W2057488242 hasConcept C165205528 @default.
- W2057488242 hasConcept C172707124 @default.
- W2057488242 hasConcept C174598085 @default.
- W2057488242 hasConcept C175551986 @default.
- W2057488242 hasConcept C199360897 @default.
- W2057488242 hasConcept C24890656 @default.
- W2057488242 hasConcept C2775846686 @default.
- W2057488242 hasConcept C2779843651 @default.
- W2057488242 hasConcept C41008148 @default.
- W2057488242 hasConcept C50644808 @default.
- W2057488242 hasConcept C58166 @default.
- W2057488242 hasConcept C84462506 @default.
- W2057488242 hasConcept C9390403 @default.
- W2057488242 hasConceptScore W2057488242C104267543 @default.
- W2057488242 hasConceptScore W2057488242C119599485 @default.
- W2057488242 hasConceptScore W2057488242C119857082 @default.
- W2057488242 hasConceptScore W2057488242C121332964 @default.
- W2057488242 hasConceptScore W2057488242C12267149 @default.
- W2057488242 hasConceptScore W2057488242C127313418 @default.
- W2057488242 hasConceptScore W2057488242C127413603 @default.
- W2057488242 hasConceptScore W2057488242C152745839 @default.