FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS
- 1 March 2002
- journal article
- Published by Elsevier in Mechanical Systems and Signal Processing
- Vol. 16 (2-3), 373-390
- https://doi.org/10.1006/mssp.2001.1454
Abstract
No abstract availableThis publication has 11 references indexed in Scilit:
- Comparison of algorithms that select features for pattern classifiersPattern Recognition, 2000
- Genetic algorithms for feature selection in machine condition monitoring with vibration signalsIEE Proceedings - Vision, Image, and Signal Processing, 2000
- A Tutorial on Support Vector Machines for Pattern RecognitionData Mining and Knowledge Discovery, 1998
- Real-time classification of rotating shaft loading conditions using artificial neural networksIEEE Transactions on Neural Networks, 1997
- Feature selection: evaluation, application, and small sample performanceIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Genetic algorithms and their applicationsIEEE Signal Processing Magazine, 1996
- A COMPARISON OF AUTOREGRESSIVE MODELING TECHNIQUES FOR FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGSMechanical Systems and Signal Processing, 1996
- Automated fault detection and accommodation: a learning systems approachIEEE Transactions on Systems, Man, and Cybernetics, 1995
- Intelligent monitoring of ball bearing conditionsMechanical Systems and Signal Processing, 1992
- Neural networks in process fault diagnosisIEEE Transactions on Systems, Man, and Cybernetics, 1991