A deep learning method using SDA combined with dropout for bearing fault diagnosis

Vibroengineering Procedia

Volumn 5, Issue , 2015, Pages 151-156

  Zhao, Wanlin ^a   Lu, Chen ^a   Ma, Jian ^b   Wang, Zili ^a  

^a BEIHANG UNIVERSITY   (China)

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

Author keywords

Bearing; Deep learning; Dropout; Fault diagnosis; Stacked denoising autoencoder

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DEEP LEARNING; FAULT DETECTION; ROLLER BEARINGS;

AUTO ENCODERS; BEARING; DE-NOISING; DEEP LEARNING; DROPOUT; FAULT CLASSIFICATION; FAULTS DIAGNOSIS; LEARNING METHODS; ROLLING BEARINGS; STACKED DENOISING AUTOENCODER;

FAILURE ANALYSIS;

EID: 84954206695     PISSN: 23450533     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

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