Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

IEEE Access

Volumn 5, Issue , 2017, Pages 15066-15079

  Qi, Yumei ^a   Shen, Changqing ^a   Wang, Dong ^b   Shi, Juanjuan ^a   Jiang, Xingxing ^a   Zhu, Zhongkui ^a  

^a SOOCHOW UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

autoregressive model; ensemble empirical mode decomposition; fault diagnosis; Sparse autoencoder

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; FAILURE ANALYSIS; FEATURE EXTRACTION; LEARNING SYSTEMS; MACHINERY; NETWORK ARCHITECTURE; REDUNDANCY; ROTATING MACHINERY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

AUTO ENCODERS; AUTO REGRESSIVE MODELS; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); FAULT DIAGNOSIS OF ROTATING MACHINERIES; INTRINSIC MODE FUNCTIONS; MACHINE LEARNING METHODS; MACHINERY FAULT DIAGNOSIS;

FAULT DETECTION;

EID: 85028453210     PISSN: None     EISSN: 21693536     Source Type: Journal    
DOI: 10.1109/ACCESS.2017.2728010     Document Type: Article

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