Fault diagnosis of ball bearings using continuous wavelet transform

Applied Soft Computing Journal

Volumn 11, Issue 2, 2011, Pages 2300-2312

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

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Artificial neural network; Energy to Shannon Entropy ratio; Relative Wavelet Energy; Self organizing maps; Support vector machine

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BEARING FAILURES; BEARING FAULT; CLASSIFICATION EFFICIENCY; COMPLEX MORLET WAVELET; CONTINUOUS WAVELET TRANSFORM; CONTINUOUS WAVELET TRANSFORMS; DIAGNOSIS PERFORMANCE; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; MACHINE LEARNING TECHNIQUES; MEYER WAVELET; ROLLING ELEMENT BEARING; ROTATING MACHINE; SHANNON ENTROPY; STATISTICAL FEATURES; SUPERVISED MACHINE LEARNING; SVM CLASSIFIERS; TEST RESULTS; UNSUPERVISED MACHINE LEARNING; VIBRATION SIGNAL; WAVELET COEFFICIENTS; WAVELET ENERGY; WAVELET SELECTION;

BALL BEARINGS; CONFORMAL MAPPING; ENTROPY; FEATURE EXTRACTION; FLIGHT DYNAMICS; LEARNING ALGORITHMS; ROLLING; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES;

WAVELET TRANSFORMS;

EID: 78751629036     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2010.08.011     Document Type: Conference Paper

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