Use of particle swarm optimization for machinery fault detection

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 2, 2009, Pages 308-316

  Samanta, B ^a   Nataraj, C ^a  

^a Villanova University   (United States)

Author keywords

Computational intelligence; Feature selection; Machinery condition monitoring; Signal processing; Swarm intelligence

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); CELLULAR AUTOMATA; CLASSIFIERS; CONDITION MONITORING; ELECTRIC FAULT LOCATION; FAULT DETECTION; FEATURE EXTRACTION; GENETIC ALGORITHMS; INTELLIGENT CONTROL; LEARNING SYSTEMS; MACHINERY; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; ROTATING MACHINERY; ROTATION; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

ARTIFICIAL NEURAL NETWORKS; BEARING FAULT DETECTIONS; COMPUTATIONAL INTELLIGENCE; EXPERIMENTAL DATUM; FEATURE SELECTION; HIDDEN LAYERS; INPUT FEATURES; KERNEL PARAMETERS; MACHINERY CONDITION MONITORING; MACHINERY FAULT DETECTIONS; PARTICLE SWARMS; PRINCIPAL COMPONENTS; PSO ALGORITHMS; ROTATING MACHINES; SUPPORT VECTORS; SWARM INTELLIGENCE; TIME DOMAINS; VIBRATION DATUM; VIBRATION SIGNALS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 58949102919     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.07.006     Document Type: Article

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