Energy-Fluctuated Multiscale Feature Learning with Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

IEEE Transactions on Instrumentation and Measurement

Volumn 66, Issue 8, 2017, Pages 1926-1935

  Ding, Xiaoxi ^a   He, Qingbo ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Deep convolutional network (ConvNet); multiscale feature; spindle bearing fault diagnosis; wavelet packet energy (WPE) image

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CONVOLUTION; FAILURE ANALYSIS; IMAGE PROCESSING; IMAGE RECONSTRUCTION; NETWORK ARCHITECTURE; PHASE SPACE METHODS; VECTOR SPACES;

CLASSIFICATION ACCURACY; CONVOLUTIONAL NETWORKS; GLOBAL AND LOCAL INFORMATIONS; INTELLIGENT FAULT DIAGNOSIS; MULTI-CLASS CLASSIFICATION; PHASE SPACE RECONSTRUCTION; WAVELET PACKET ENERGIES; WAVELET PACKET TRANSFORMS;

FAULT DETECTION;

EID: 85015852225     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2017.2674738     Document Type: Article

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