Classification of acoustic emission signals using wavelets and Random Forests: Application to localized corrosion

Mechanical Systems and Signal Processing

Volumn 70-71, Issue , 2016, Pages 1026-1037

  Morizet, N ^a   Godin, N ^a   Tang, J ^a   Maillet, E ^a   Fregonese, M ^a   Normand, B ^a  

^a UNIVERSITÉ DE LYON   (France)

Author keywords

Acoustic emission; Corrosion monitoring; Machine learning; Random Forests; Supervised classification; Wavelets

Indexed keywords

ACOUSTIC EMISSIONS; ARTIFICIAL INTELLIGENCE; CHEMICAL INDUSTRY; CORROSION; DECISION TREES; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; SUPERVISED LEARNING;

ACOUSTIC EMISSION SIGNAL; CORROSION MONITORING; GROUND TRUTH DATA; K NEAREST NEIGHBOR (K NN) ALGORITHM; LOCALIZED CORROSION; RANDOM FORESTS; SUPERVISED CLASSIFICATION; WAVELETS;

ACOUSTIC EMISSION TESTING;

EID: 84961163563     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.09.025     Document Type: Article

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