教授 博士生导师
招生学科专业:
交通运输工程 -- 【招收博士、硕士研究生】 -- 民航学院
电子信息 -- 【招收博士、硕士研究生】 -- 民航学院
交通运输 -- 【招收博士、硕士研究生】 -- 民航学院
毕业院校:中国人民解放军国防科学技术大学
学历:国防科学技术大学
学位:博士学位
所在单位:民航学院
办公地点:民航学院办公楼1103房间
联系方式:15062211551
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所属单位:民航学院
发表刊物:Zhendong Ceshi Yu Zhenduan
摘要:Considering the state parameters significant nonlinearity and the vulnerability to noise pollution in the aero-engine gas path faults,a method based on denoising autoencoder (DAE) and integrated with a neural networks of firefly algorithm (FA) and radial basis function (RBF) is proposed to diagnose the gas path faults and improve the diagnostic accuracy. The DAE is adopted through greedy algorithms to identify deeper robust features that helps diagnose the faults. To further improve the diagnostic accuracy of the algorithm, inertia weight and improved FA of self-adaptive light intensity factor are introduced to obtain the firefly radial basis function (FRBF) network after optimizing the RBF network. Then the robust features extracted from the DAE are imported into the FRBF for faults diagnosis. Based on practices, the extracting method is compared with the algorithms which are original DAE, independent FRBF, SVM and RBF. According to the results, the proposed method presents highest diagnostic accuracy of 98.1%, stable performance in the algorithms and more satisfying robustness. © 2019, Editorial Department of JVMD. All right reserved.
ISSN号:1004-6801
是否译文:否
发表时间:2019-06-01
合写作者:Hong, Jiyu,Che, Changchang,Ni, Xiaomei
通讯作者:王华伟