A lighted deep convolutional neural network based fault diagnosis of rotating machinery

Sensors (Switzerland)

Volumn 19, Issue 10, 2019, Pages

  Ma, Shangjun ^a   Cai, Wei ^a   Liu, Wenkai ^b   Shang, Zhaowei ^b   Liu, Geng ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b CHONGQING UNIVERSITY   (China)

Author keywords

Convolutional neural networks; Deep learning; Fault diagnosis; Rotating machinery; Wavelet packet transform

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CONVOLUTION; DATA MINING; DEEP LEARNING; FAILURE ANALYSIS; FAULT DETECTION; MACHINE DESIGN; NEURAL NETWORKS; ROTATING MACHINERY; WAVELET ANALYSIS; WAVELET DECOMPOSITION;

CASE WESTERN RESERVE UNIVERSITY; CLASSIFICATION PERFORMANCE; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; FAULT DIAGNOSIS OF ROTATING MACHINERIES; MULTI-SCALE INFORMATIONS; SUPPORT VECTOR MACHINE ALGORITHM; WAVELET PACKET TRANSFORMS;

DEEP NEURAL NETWORKS;

ARTICLE; DECOMPOSITION; DEEP LEARNING; MEMORY; NOISE; SIGNAL PROCESSING; SUPPORT VECTOR MACHINE; TRANSFER OF LEARNING; VIBRATION;

EID: 85067299389     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s19102381     Document Type: Article

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