A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE: Fid-Bau Portal

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A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE

WU YinHua / XU QiongYan

Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficiently

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A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE

WU YinHua / XU QiongYan

Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficiently

A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE

WU YinHua / XU QiongYan

Aiming at the fact that the fault diagnosis performance of support vector machine（ SVM） highly depends on the parameters selection,a fault diagnosis method based on improved artificial bee colony（ IABC） optimize SVM was proposed. In order to improve search ability of ABC,Levy flight strategy was introduced and improved the original ABC algorithm. Use the IABC to optimize SVM parameters can effectively improve the classification performance of SVM. Different fault type and different fault degree of rolling bearing fault diagnosis experiment results show that the IABC can obtain better parameters when compared with ABC,GA and PSO,improved the fault diagnosis accuracy of SVM and can applied to fault diagnosis efficiently

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Titel:

A FAULT DIAGNOSIS METHOD BASED ON IMPROVED ARTIFICAL BEE COLONY OPTIMIZE SUPPORT VECTOR MACHINE

Beteiligte:

WU YinHua (Autor:in) / XU QiongYan (Autor:in)

Erschienen in:

Jixie qiangdu, Vol 40, Pp 287-292 (2018)

Erscheinungsdatum:

2018

ISSN:

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

Schlagwörter:

Artificial bee colony , Levy flight , Support vector machine , Parameters optimization , Fault diagnosis , Mechanical engineering and machinery , TJ1-1570 , Materials of engineering and construction. Mechanics of materials , TA401-492

Metadata by DOAJ is licensed under CC BY-SA 1.0

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