Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine
Top Cited Papers
- 1 October 2007
- journal article
- research article
- Published by Elsevier in Mechanical Systems and Signal Processing
- Vol. 21 (7), 2933-2945
- https://doi.org/10.1016/j.ymssp.2007.02.003
Abstract
No abstract availableKeywords
This publication has 21 references indexed in Scilit:
- Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systemsApplied Soft Computing, 2007
- Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machinesMechanical Systems and Signal Processing, 2006
- Bearing fault diagnosis based on wavelet transform and fuzzy inferenceMechanical Systems and Signal Processing, 2004
- Artificial neural networks and support vector machines with genetic algorithm for bearing fault detectionEngineering Applications of Artificial Intelligence, 2003
- FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMSMechanical Systems and Signal Processing, 2002
- MULTIPLE BAND-PASS AUTOREGRESSIVE DEMODULATION FOR ROLLING-ELEMENT BEARING FAULT DIAGNOSISMechanical Systems and Signal Processing, 2001
- Neural-network-based motor rolling bearing fault diagnosisIEEE Transactions on Industrial Electronics, 2000
- Helicopter Gearbox Fault Detection: A Neural Network Based ApproachJournal of Vibration and Acoustics, 1999
- De-noising by soft-thresholdingIEEE Transactions on Information Theory, 1995
- Entropy-based algorithms for best basis selectionIEEE Transactions on Information Theory, 1992