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:Hangkong Dongli Xuebao
摘要:The accurate maintenance level decision can avoid the excessive maintenance and shortage of maintenance and save maintenance cost on the premise of ensuring the safe operation of aero-engine. To achieve the classification and prediction for maintenance level, monitor information and characteristics of maintenance level, using algorithm of deep belief network(DBN), were combined, excavating deep relationship between the condition monitoring and maintenance level decision making. The model can extract sample feature from DBN pretreatment and back propagation (BP) neural network reverse fine-tuning and improve the forecast accuracy of maintenance level. Taking the state parameters and maintenance level data of an airline CF6 engine as example, the analysis results showed that the model could excavate the deeper information of the sample through the construction of multi-layer network structure, which was superior to the traditional neural network in the classification ability and the accuracy of decision-making. So the model had strong ability of feature extraction and higher classification accuracy for the maintenance level. The model was able to get more accurate results maintenance level decision and avoided unnecessary losses due to misclassification of maintenance level. © 2018, Editorial Department of Journal of Aerospace Power. All right reserved.
ISSN号:1000-8055
是否译文:否
发表时间:2018-06-01
合写作者:Che, Changchang,刘伟强
通讯作者:王华伟