Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network

IEEE Transactions on Instrumentation and Measurement

Volumn 66, Issue 7, 2017, Pages 1693-1702

  Chen, Zhuyun ^a   Li, Weihua ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Bearings; Deep belief network (DBN); Fault diagnosis; Sensor fusion; Sparse autoencoder (SAE)

Indexed keywords

DATA FUSION; FAILURE ANALYSIS; FREQUENCY DOMAIN ANALYSIS; LEARNING SYSTEMS; MACHINERY; TIME DOMAIN ANALYSIS;

ALGORITHM VALIDATION; BEARING FAULT DIAGNOSIS; DEEP BELIEF NETWORK (DBN); DIAGNOSIS RELIABILITY; ENVIRONMENTAL INTERFERENCE; FEATURE FUSION METHOD; MULTISENSOR DATA FUSION; STATISTICAL FEATURES;

FAULT DETECTION;

EID: 85016125628     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2017.2669947     Document Type: Article

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