Fault Diagnosis for a Wind Turbine Generator Bearing via Sparse Representation and Shift-Invariant K-SVD

IEEE Transactions on Industrial Informatics

Volumn 13, Issue 3, 2017, Pages 1321-1331

  Yang, Boyuan ^a   Liu, Ruonan ^a   Chen, Xuefeng ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Fault diagnosis; periodical impulse vibration extraction; redundant union of dictionaries; shift invariant dictionary learning; sparse time frequency representation; wind turbine generator

Indexed keywords

ATOMS; EXTRACTION; FAILURE ANALYSIS; FEATURE EXTRACTION; SIGNAL PROCESSING; TURBINES; TURBOGENERATORS; WIGNER-VILLE DISTRIBUTION; WIND TURBINES;

DATA-DRIVEN FAULT DIAGNOSIS; DICTIONARY LEARNING; NON-STATIONARY CONDITION; PHYSICAL STRUCTURES; SPARSE REPRESENTATION; STATE-OF-THE-ART METHODS; TIME FREQUENCY ANALYSIS; TIME-FREQUENCY REPRESENTATIONS;

FAULT DETECTION;

EID: 85020641208     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2017.2662215     Document Type: Article

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