Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines

Author:

Fatima S.,Guduri B.,Mohanty A.R.,Naikan V.N.A.

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference25 articles.

1. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique;Lei;Mech. Syst. Signal Process.,2008

2. Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing;Sugumaran;Mech. Syst. Signal Process.,2007

3. PCA-based feature selection scheme for machine defect classification;Malhi;IEEE Trans. Instrum. Meas.,2004

4. S. Fatima, A.R. Mohanty, V.N.A. Naikan, Most effective transducer locations for permanent health monitoring of a rotating machine, in: Proceedings of the 20th International Congress on Sound and Vibration Bangkok, Thailand, 2013.

5. Technique for optical placement of transducers for fault detection in rotating machines;Fatima;J. Risk Reliab.,2013

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