Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder

IET Science, Measurement and Technology

Volumn 11, Issue 6, 2017, Pages 687-695

  Xia, Min ^a   Li, Teng ^a   Liu, Lizhi ^b   Xu, Lin ^b   de Silva, Clarence W ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b Istuary Innovation Group   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITION MONITORING; DEEP LEARNING; DEEP NEURAL NETWORKS; FAILURE ANALYSIS; LEARNING SYSTEMS;

ARTIFICIAL INTELLIGENCE ALGORITHMS; DATA-DRIVEN APPROACH; FAULT CLASSIFICATION; INTELLIGENT FAULT DIAGNOSIS; OPERATIONAL SAFETY; SYSTEM CONDITIONS; TRADITIONAL APPROACHES; UNSUPERVISED FEATURE LEARNING;

FAULT DETECTION;

EID: 85028984900     PISSN: 17518822     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-smt.2016.0423     Document Type: Article

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