GEARBOX DIAGNOSIS BASED ON SVM OPTIMIZED BY DOUBLE GROUP COEVOLUTION FRUIT FLY OPTIMIZATION ALGORITHM

Autor:	LEI Biao, HUI EnMing, GUAN HaiYing, WANG XiaoJun
Jazyk:	čínština
Rok vydání:	2022
Předmět:	Fruit fly optimization algorithm Coevolution Support vector machine Gearbox Fault diagnosis Mechanical engineering and machinery TJ1-1570 Materials of engineering and construction. Mechanics of materials TA401-492
Zdroj:	Jixie qiangdu, Vol 44, Pp 753-757 (2022)
Druh dokumentu:	article
ISSN:	1001-9669 75451883
DOI:	10.16579/j.issn.1001.9669.2022.03.035
Popis:	In order to improve the optimization effect of the fruit fly optimization algorithm（FOA） on support vector machine（SVM） parameters, the evolution strategy of FOA was improved, and the duoble group coevolution fruit fly optimization algorithm（DGCFOA） was proposed in this paper. The DGCFOA was used to optimize the parameters of SVM and then used to gearbox fault diagnosis. Diagnosis results show that DGCFOA algorithm can obtained better SVM parameters when compared with FOA, it significantly improved fault diagnosis accuracy of gearbox. In addition, the diagnosis results also show that DGCFOA has higher diagnostic accuracy and more obvious advantages when compared with some other methods.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8b057b0d9361455a9cf46cbd75451883 Zobrazit plný text záznamu View record in DOAJ