EXTRACTING USEFUL HIGHER-ORDER FEATURES FOR CONDITION MONITORING USING ARTIFICIAL NEURAL NETWORKS

Authors
MURRAY A PENMAN J
Citation
A. Murray et J. Penman, EXTRACTING USEFUL HIGHER-ORDER FEATURES FOR CONDITION MONITORING USING ARTIFICIAL NEURAL NETWORKS, IEEE transactions on signal processing, 45(11), 1997, pp. 2821-2828
Citations number
14
Categorie Soggetti
Engineering, Eletrical & Electronic
Journal title
IEEE transactions on signal processing → ACNP
ISSN journal
1053587X
Volume
45
Issue
11
Year of publication
1997
Pages
2821 - 2828
Database
ISI
SICI code
1053-587X(1997)45:11<2821:EUHFFC>2.0.ZU;2-O
Abstract
Vibration data from an induction machine is employed to investigate hi gher order properties associated with electrical machine faults, Three fault conditions are investigated together with all possible permutat ions, By considering combinations of faults, interesting higher order properties are identified and presented, ultimately resulting in impro ved ANN diagnoses of faults.