Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings

Shock and Vibration

Volumn 2017, Issue , 2017, Pages

  Verstraete, David ^a   Ferrada, Andrés ^b   Droguett, Enrique López ^a,b   Meruane, Viviana ^b   Modarres, Mohammad ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

BEARINGS (MACHINE PARTS); COMPUTER AIDED DIAGNOSIS; DEEP LEARNING; DEEP NEURAL NETWORKS; FAILURE ANALYSIS; FEATURE EXTRACTION; MATHEMATICAL TRANSFORMATIONS; NETWORK ARCHITECTURE; NEURAL NETWORKS; ROLLER BEARINGS; VIBRATION MEASUREMENT; WAVELET TRANSFORMS;

DEEP CONVOLUTIONAL NEURAL NETWORKS; FEATURE EXTRACTION AND SELECTION; HILBERT HUANG TRANSFORMS; LABOR INTENSIVE PROCESS; ROLLING ELEMENT BEARING; SHORT TIME FOURIER TRANSFORMS; TIME-FREQUENCY ANALYSIS METHODS; TIME-FREQUENCY REPRESENTATIONS;

FAULT DETECTION;

EID: 85037847842     PISSN: 10709622     EISSN: None     Source Type: Journal    
DOI: 10.1155/2017/5067651     Document Type: Article

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