Fault Diagnosis Model Based on Support Vector Machine and Genetic Algorithm

Abstract:

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Recently, the dominating difficulty that fault intelligent diagnosis system faces is terrible lack of typical fault samples, which badly prohibits the development of machinery fault intelligent diagnosis. Mainly according to the key problems of support vector machine need to resolve in fault intelligent diagnosis system, this paper makes more systemic and thorough researches in building fault classifiers, parameters optimization of kernel function. A decision directed acyclic graph fault diagnosis classification model based on parameters selected by genetic algorithm is proposed, abbreviated as GDDAG. Finally, GDDAG model is applied to rotor fault system, the testing results demonstrate that this model has very good classification precision and realizes the multi-faults diagnosis.

Info:

Periodical:

Edited by:

Han Zhao

Pages:

2535-2539

DOI:

10.4028/www.scientific.net/AMM.130-134.2535

Citation:

W. Niu et al., "Fault Diagnosis Model Based on Support Vector Machine and Genetic Algorithm", Applied Mechanics and Materials, Vols. 130-134, pp. 2535-2539, 2012

Online since:

October 2011

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Price:

$35.00

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