A hybrid feature selection scheme and self-organizing map model for machine health assessment
- 31 July 2011
- journal article
- Published by Elsevier BV in Applied Soft Computing
- Vol. 11 (5), 4041-4054
- https://doi.org/10.1016/j.asoc.2011.03.026
Abstract
No abstract availableKeywords
This publication has 20 references indexed in Scilit:
- A novel method for machine performance degradation assessment based on fixed cycle features testJournal of Sound and Vibration, 2009
- Feature-based classifier ensembles for diagnosing multiple faults in rotating machineryApplied Soft Computing, 2008
- A review on machinery diagnostics and prognostics implementing condition-based maintenanceMechanical Systems and Signal Processing, 2006
- Intelligent prognostics tools and e-maintenanceComputers in Industry, 2006
- PCA-Based Feature Selection Scheme for Machine Defect ClassificationIEEE Transactions on Instrumentation and Measurement, 2004
- Degradation Assessment and Fault Modes Classification Using Logistic RegressionJournal of Manufacturing Science and Engineering, 2004
- Robust performance degradation assessment methods for enhanced rolling element bearing prognosticsAdvanced Engineering Informatics, 2003
- ROLLING ELEMENT BEARING DIAGNOSTICS IN RUN-TO-FAILURE LIFETIME TESTINGMechanical Systems and Signal Processing, 2001
- Neural-network-based motor rolling bearing fault diagnosisIEEE Transactions on Industrial Electronics, 2000
- A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearingsTribology International, 1999