Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines-Reference-Cited by-同舟云学术

Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

Published:2006-10 Issue:7 Volume:20 Page:1523-1536
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Rojas Alfonso,Nandi Asoke K.

Publisher

Elsevier BV

Subject

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

Reference17 articles.

1. Support vector machines for detection and characterization of rolling element bearing faults;Jack;Proceedings of Institution of Mechanical Engineers, Part C,2002

2. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms;Jack;Mechanical Systems and Signal Processing,2002

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

4. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur;Li;Pattern Recognition Letters,2002

5. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines;Yang;Mechanical Systems and Signal Processing,2005

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