Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform

Neurocomputing

Volumn 110, Issue , 2013, Pages 9-17

  Kankar, P K ^a   Sharma, Satish C ^b   Harsha, S P ^b  

^a PDPM INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Artificial neural network (ANN); Cyclic autocorrelation; Cyclostationarity; Energy to Shannon Entropy ratio; Self organizing maps (SOM); Support vector machine (SVM); Wavelets

Indexed keywords

CYCLIC AUTOCORRELATION; CYCLOSTATIONARITY; SELF-ORGANIZING MAP (SOM); SHANNON ENTROPY; WAVELETS;

AUTOCORRELATION; CONFORMAL MAPPING; NEURAL NETWORKS; SIGNAL PROCESSING; SOFT COMPUTING; WAVELET TRANSFORMS;

SUPPORT VECTOR MACHINES;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ANALYSIS; CYCLIC AUTOCORRELATION; CYCLOSTATIONARY ANALYSIS; ENERGY; ENERGY TO SHANNON ENTROPY RATIO; ENTROPY; FREQUENCY MODULATION; INTERMETHOD COMPARISON; MACHINE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; ROLLING ELEMENT BEARING FAULT DIAGNOSIS; SELF ORGANIZING MAP; SIGNAL TRANSDUCTION; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; VIBRATION; WAVELET TRANSFORM;

EID: 84876134288     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.012     Document Type: Article

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