A fuzzy clustering-based binary threshold bispectrum estimation approach

Neural Computing and Applications

Volumn 21, Issue SUPPL. 1, 2012, Pages 385-392

  Liu, W Y ^a   Han, J G ^a  

^a JIANGSU NORMAL UNIVERSITY   (China)

Author keywords

Bearing fault; Bispectrum estimation; Fault recognition; Fuzzy clustering

Indexed keywords

BEARING FAULT; BINARIZATION THRESHOLD; BINARY FEATURES; BINARY THRESHOLD; BISPECTRUM; DAMAGED COMPONENTS; DE-NOISE; ESTIMATION RESULTS; FAULT RECOGNITION; FUZZY CLUSTERING TECHNIQUES; HIGHER ORDER CUMULANTS; HIGHER ORDER SPECTRUM; KERNEL IMAGE; MECHANICAL FAULTS; NOISE JAMMING; NONLINEAR CHARACTERISTICS; NONLINEAR COUPLING; OBJECT CLASSIFICATION; ROLLING BEARINGS; ROLLING ELEMENT BEARING; TEST SAMPLES; TRAINING SAMPLE; VIBRATION SIGNAL;

BEARINGS (MACHINE PARTS); FUZZY CLUSTERING; PATTERN RECOGNITION; SAMPLING;

ESTIMATION;

EID: 84865799912     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1050-y     Document Type: Article

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