Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Cognitive Systems Research

Volumn 53, Issue , 2019, Pages 42-50

  Hoang, Duy Tang ^a   Kang, Hee Jun ^a  

^a University of Ulsan   (South Korea)

Author keywords

Bearing fault diagnosis; Convolutional neural network; Deep learning; Machine learning

Indexed keywords

CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DATA MINING; DEEP LEARNING; DEEP NEURAL NETWORKS; ELECTRIC FAULT CURRENTS; EXTRACTION; FAILURE ANALYSIS; FEATURE EXTRACTION; INPUT OUTPUT PROGRAMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; ROLLER BEARINGS; SIGNAL PROCESSING;

AUTOMATIC FAULT DIAGNOSIS; BEARING FAULT DIAGNOSIS; FEATURE EXTRACTION TECHNIQUES; INTELLIGENT FAULT DIAGNOSIS; LEARNING ARCHITECTURES; MACHINE HEALTH MONITORING; ROLLING ELEMENT BEARING; SIGNAL PROCESSING TECHNIQUE;

FAULT DETECTION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BEARING FAULT DIAGNOSIS; CLASSIFICATION; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; FEATURE EXTRACTION; IMAGE ANALYSIS; MEASUREMENT ACCURACY; NOISE; PHYSICAL PARAMETERS; PRIORITY JOURNAL; ROLLING ELEMENT BEARING; VIBRATION;

EID: 85044258456     PISSN: None     EISSN: 13890417     Source Type: Journal    
DOI: 10.1016/j.cogsys.2018.03.002     Document Type: Article

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