Long Short-Term Memory Network for Integrated Modular Avionics Degradation Modeling and Health Assessment

被引：2

作者：

Guo, Yingchao ^{[1
]}

Chen, Jie ^{[1
]}

Zhong, Yichen ^{[1
]}

Shen, Chi ^{[1
]}

Zhao, Yuyang ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Civil Aviat, Xian, Peoples R China

来源：

2022 IEEE 2ND INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND ARTIFICIAL INTELLIGENCE (CCAI 2022) | 2022年

关键词：

Integrated Modular Avionics; Long Short-Term Memory; Degradation Modeling; Health Assessment;

D O I：

10.1109/CCAI55564.2022.9807807

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the improvement of aircraft informatization, Integrated Modular Avionics (IMA) system has become an important part of modern aircraft airborne systems, and its operation status has great significance to ensure flight safety, therefore, it is necessary to study its degradation process and health assessment. Based on the IMA system analysis and health state classification, the Long Short-Term Memory (LSTM) network is introduced in this paper to model the IMA system's degradation process and assess system health status, the effectiveness of the proposed method for degradation modeling and health assessment is verified by the experimental simulation in the end.

引用

页码：38 / 42

页数：5

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