Capturing High-Discriminative Fault Features for Electronics-Rich Analog System via Deep Learning

IEEE Transactions on Industrial Informatics

Volumn 13, Issue 3, 2017, Pages 1213-1226

  Liu, Zhenbao ^a   Jia, Zhen ^a   Vong, Chi Man ^b   Bu, Shuhui ^a   Han, Junwei ^a   Tang, Xiaojun ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITY OF MACAU   (Macao)

Author keywords

Analog circuits; deep belief network; deep learning; diagnosis; failure; fault; restricted Boltzmann machines

Indexed keywords

ANALOG CIRCUITS; CLASSIFICATION (OF INFORMATION); COMPUTER SYSTEM RECOVERY; DEEP LEARNING; DIAGNOSIS; E-LEARNING; EXTRACTION; FAILURE ANALYSIS; FAULTING; FEATURE EXTRACTION; FREQUENCY DOMAIN ANALYSIS; SEMANTICS; SIGNAL PROCESSING;

DEEP BELIEF NETWORKS; FAULT DETECTION AND ISOLATION; FEATURE EXTRACTION METHODS; MULTI-CLASS CLASSIFICATION; RESTRICTED BOLTZMANN MACHINE; SEMANTIC REPRESENTATION; SOPHISTICATED MECHANISM; TIME-FREQUENCY SIGNAL PROCESSING;

FAULT DETECTION;

EID: 85020682598     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2017.2690940     Document Type: Article

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