Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum

JVC/Journal of Vibration and Control

Volumn 22, Issue 10, 2016, Pages 2420-2433

  Jiang, Bingzhen ^a   Xiang, Jiawei ^a   Wang, Yanxue ^b  

^a WENZHOU UNIVERSITY   (China)

^b GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

Author keywords

Cyclic bispectrum; fault diagnosis; optimal cyclic frequency; probabilistic principal component analysis; rolling bearings

Indexed keywords

FAILURE ANALYSIS; FAULT DETECTION; NUMERICAL METHODS; SIGNAL TO NOISE RATIO;

BEARING FAULT DIAGNOSIS; BEARINGS FAULT DIAGNOSIS; CYCLIC BISPECTRUM; CYCLIC FREQUENCY; EXPERIMENTAL INVESTIGATIONS; PRINCIPAL COMPONENT MODELS; PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS; ROLLING BEARINGS;

ROLLER BEARINGS;

EID: 84971379024     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546314547533     Document Type: Article

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