Fault diagnosis of civil aero-engine driven by unbalanced samples based on DBN

被引：0

作者：

Zhong S. ^{[1
,2
]}

Li X. ^{[2
]}

Zhang Y. ^{[2
]}

机构：

[1] School of Mechatronics Engineering, Harbin Institute of Technology, Harbin

[2] School of Naval Architecture and Ocean Engineering, Harbin Institute of Technology(Weihai), Weihai, 264200, Shandong

来源：

Hangkong Dongli Xuebao/Journal of Aerospace Power | 2019年 / 34卷 / 03期

关键词：

Adaboost.M1; algorithm; Civil aero-engine; Deep belief network; Fault diagnosis; Unbalanced samples;

D O I：

10.13224/j.cnki.jasp.2019.03.024

中图分类号：

学科分类号：

摘要：

Through combination of deep belief network (DBN), sampling and integration technology, a fault diagnosis model of civil aero-engine based on unbalanced sample driving was proposed. By analyzing the historical flight data of civil aero-engines, the model used DBN to extract the internal features of the performance parameters, then used the sampling technology to equalize the unbalanced samples, and finally adopted integrated technology for fault classification. The model was applied to historical flight data of CFM56-7B series engines. Compared with common fault diagnosis methods, the experimental results showed that the model had higher accuracy of 0.996 and AUC value of 0.948, and can effectively deal with high-dimensional and unbalanced problems of civil aero-engine samples. © 2019, Editorial Department of Journal of Aerospace Power. All right reserved.

引用

页码：708 / 716

页数：8