A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings

JVC/Journal of Vibration and Control

Volumn 21, Issue 16, 2015, Pages 3123-3131

  Vakharia, V ^a   Gupta, V K ^a   Kankar, P K ^a  

^a PDPM INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

artificial neural network; Fault diagnosis; permutation entropy; support vector machine; wavelet selection

Indexed keywords

BALL BEARINGS; COMPUTER AIDED DIAGNOSIS; DEFECTS; ENTROPY; FAILURE ANALYSIS; FEATURE EXTRACTION; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; TIME DOMAIN ANALYSIS;

ARTIFICIAL INTELLIGENCE TECHNIQUES; CLASSIFICATION ACCURACY; DETECTION AND DIAGNOSIS; FEATURE EXTRACTION TECHNIQUES; ON-LINE FAULT DIAGNOSIS; PERMUTATION ENTROPY; STATISTICAL PARAMETERS; WAVELET SELECTION;

FAULT DETECTION;

EID: 84947969274     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546314520830     Document Type: Article

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