A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion

Sensors (Switzerland)

Volumn 19, Issue 7, 2019, Pages

  Gong, Wenfeng ^a,b   Chen, Hui ^a   Zhang, Zehui ^a   Zhang, Meiling ^b   Wang, Ruihan ^a   Guan, Cong ^a   Wang, Qin ^b  

^a WUHAN UNIVERSITY OF TECHNOLOGY   (China)

^b GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

Author keywords

Convolutional neural network; Data fusion; Deep learning; Global average pooling; Intelligent fault diagnosis; Multichannel; Rotating machinery; Support vector machine

Indexed keywords

BACKPROPAGATION; CONVOLUTION; DATA FUSION; DATA MINING; DEEP LEARNING; FAILURE ANALYSIS; NEAREST NEIGHBOR SEARCH; NEURAL NETWORKS; ROLLER BEARINGS; ROTATING MACHINERY; SUPPORT VECTOR MACHINES;

BACK PROPAGATION NEURAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; FAULT CLASSIFICATION; FAULT DIAGNOSIS METHOD; GLOBAL AVERAGE POOLING; INTELLIGENT FAULT DIAGNOSIS; MULTICHANNEL; TEMPORAL AND SPATIAL;

FAULT DETECTION;

EID: 85064722120     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s19071693     Document Type: Article

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