南京航空航天大学学报（英文版）2020，Vol.37Issue(4)：508-516,9.
稀疏驱动的航空发动机主轴承智能监测研究
Sparsity?Assisted Intelligent Condition Monitoring Method for Aero?engine Main Shaft Bearing
摘要
Abstract
Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aero?engine. Aimed at achieving intelligent diagnosis of aero?engine main shaft bearing,an enhanced sparsity?assisted intelligent condition monitoring method is proposed in this paper. Through analyzing the weakness of convex sparsemodel,i.e. the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced?sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction. Accordingly,a sparsity?assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data. Finally,the effectiveness of the proposed method is verified through aero?engine bearing run?to?failure experiment. The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero?engine main shaft bearings.关键词
航空发动机主轴承/智能监测/特征提取/稀疏模型/变分自编码/深度学习Key words
aero‑engine main shaft bearing/intelligent condition monitoring/feature extraction/sparse model/variational autoencoders/deep learning分类
机械制造引用本文复制引用
丁宝庆,武靖耀,孙闯,王诗彬,陈雪峰,李应红..稀疏驱动的航空发动机主轴承智能监测研究[J].南京航空航天大学学报（英文版）,2020,37(4):508-516,9.基金项目
This work was supported by the Na?tional Natural Science Foundations of China(Nos.91860125,51705398),the National Key Basic Research Program of China(No. 2015CB057400)and the Shaanxi Province 2020 Natural Science Basic Research Plan(No. 2020JQ?042). （Nos.91860125,51705398）
