计算机工程2017，Vol.43Issue(7)：281-287,7.DOI:10.3969/j.issn.1000-3428.2017.07.047
基于深度学习的航空传感器故障诊断方法
Fault Diagnosis Method for Aerial Sensor Based on Deep Learning
郑晓飞 1郭创 1姚斌 1冯华鑫2
作者信息
- 1. 空军工程大学 航空航天工程学院,西安 710038
- 2. 93707部队 55分队,河北 张家口 075000
- 折叠
摘要
Abstract
To solve the problem of over-fitting and limited generalization ability during sensor fault diagnosis by traditional neural network,a fault diagnosis method for aerial sensor based on deep belief network observer is proposed.Shallow layer neural network is replaced by deep belief network.On the basis of optimizing network structure,the recurrence formula of selecting hidden layer nodes is proposed to build deep belief network state observer.Flight data is used to train deep belief network observer during offline training.Output of the observer is compared with actual output to judge the fault types and three methods of fault isolation and signal reconstruction are proposed during online diagnostics.Simulation results show that compared with BP neural network observer,the proposed method can diagnose and isolate faults and reconstruct signals with rapidity and high accuracy.关键词
航空传感器/故障诊断/深度学习/深度置信网络/故障隔离/信号重构Key words
aerial sensor/fault diagnosis/deep learning/deep belief network/fault isolation/signal reconstruction分类
信息技术与安全科学引用本文复制引用
郑晓飞,郭创,姚斌,冯华鑫..基于深度学习的航空传感器故障诊断方法[J].计算机工程,2017,43(7):281-287,7.
