Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing-Reference-Cited by-同舟云学术

Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing

Published:2007-02 Issue:2 Volume:21 Page:930-942
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Sugumaran V.,Muralidharan V.,Ramachandran K.I.

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference15 articles.

1. S.S. Wang, Artificial intelligence and expert systems for diagnostics, in: Proceedings of the Institute of International Conference on Machining and Diagnostics and Exhibition, Las Vegas, Neveda, 1989, pp. 516–512.

2. R.L. Winder, W.E. Littmann (Eds.), Bearing Damage Analysis. National Bureau of Standard Publication, April 1976.

3. Online detection of localized defects in bearing by pattern recognition analysis;James Li;ASME Journal of Engineering for Industry,1989

4. A.K. Nandi, Advanced digital vibration signal processing for condition monitoring, in: Proceedings of COMADEM2000, Houston, TX, USA, 2000, pp. 129–143.

5. Artificial neural network based fault diagnostics of rolling element bearings using time domain features;Samanta;Mechanical Systems and Signal Processing,2003

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