Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks

IEEE Transactions on Industrial Electronics

Volumn 60, Issue 8, 2013, Pages 3398-3407

  Prieto, Miguel Delgado ^a   Cirrincione, Giansalvo ^b   Espinosa, Antonio Garcia ^a   Ortega, Juan Antonio ^a   Henao, Humberto ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSITÉ DE PICARDIE JULES VERNE   (France)

Author keywords

Ball bearings; classification algorithms; condition monitoring; fault diagnosis; feature extraction; induction motors; neural networks; vibrations

Indexed keywords

BALL BEARINGS; DEFECTS; ELECTRIC MACHINERY; FAILURE ANALYSIS; FEATURE EXTRACTION; INDUCTION MOTORS; NEURAL NETWORKS;

BEARING FAULT DETECTION; CLASSIFICATION ALGORITHM; CURVILINEAR COMPONENT ANALYSIS; DIFFERENT OPERATING CONDITIONS; HIERARCHICAL NEURAL NETWORKS; NON-LINEAR MANIFOLD LEARNING; PHYSICAL PHENOMENA; VIBRATIONS;

CONDITION MONITORING;

EID: 84876248148     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2012.2219838     Document Type: Article

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