Sparse representation based on adaptive multiscale features for robust machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Volumn 229, Issue 12, 2015, Pages 2303-2313

  Zhu, Huijie ^a   Wang, Xinqing ^a   Zhao, Yang ^b   Li, Yanfeng ^a   Wang, Wenfu ^a   Li, Liping ^b  

^a PLA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b People's Liberation Army   (China)

Author keywords

dictionary learning; evidence theory; fault diagnosis; global and local features; K SVD; Sparse representation

Indexed keywords

FAILURE ANALYSIS; FEATURE EXTRACTION; MACHINERY;

ADAPTIVE FEATURE EXTRACTIONS; DICTIONARY LEARNING; ENGINEERING APPLICATIONS; EVIDENCE THEORIES; GLOBAL AND LOCAL INFORMATIONS; LOCAL FEATURE; MACHINERY FAULT DIAGNOSIS; SPARSE REPRESENTATION;

FAULT DETECTION;

EID: 84937851164     PISSN: 09544062     EISSN: 20412983     Source Type: Journal    
DOI: 10.1177/0954406214557343     Document Type: Article

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