Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification

Advanced Engineering Informatics

Volumn 32, Issue , 2017, Pages 139-151

  Lu, Chen ^a,b   Wang, Zhenya ^a,b   Zhou, Bo ^a,b  

^a BEIHANG UNIVERSITY   (China)

^b SCIENCE AND TECHNOLOGY ON RELIABILITY AND ENVIRONMENTAL ENGINEERING LABORATORY   (China)

Author keywords

Convolutional neural network; Fault diagnosis; Rolling bearing

Indexed keywords

BACKPROPAGATION; BEARINGS (MACHINE PARTS); CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; COMPUTATION THEORY; COMPUTER AIDED DIAGNOSIS; CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; FAILURE ANALYSIS; FEATURE EXTRACTION; IMAGE RECOGNITION; NEURAL NETWORKS; ROLLER BEARINGS; SYSTEMS ENGINEERING; VIBRATIONS (MECHANICAL);

BEARING FAULT DIAGNOSIS; CONVOLUTION COMPUTATIONS; CONVOLUTIONAL NEURAL NETWORK; ELECTROMECHANICAL SYSTEMS; FEED-FORWARD BACK PROPAGATION; INTELLIGENT FAULT DIAGNOSIS; NON STATIONARY CHARACTERISTICS; ROLLING BEARINGS;

FAULT DETECTION;

BEARINGS; DEFECTS; DIAGNOSIS; NEURAL NETWORKS; ROLLING;

EID: 85013801625     PISSN: 14740346     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aei.2017.02.005     Document Type: Article

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