基于多阶段注意力机制的多种导航传感器故障识别研究

王亚朝; 赵伟; 徐海洋; 刘建业

doi:10.16383/j.aas.c190435

基于多阶段注意力机制的多种导航传感器故障识别研究

doi: 10.16383/j.aas.c190435

1.
南京航空航天大学自动化学院南京 211106

基金项目: 国家自然科学基金(61533008, 61374115, 61603181), 中央高校基本科研业务费专项基金(NS2018021), 江苏高校优势学科建设工程项目资助

详细信息

作者简介:
王亚朝：南京航空航天大学自动化学院硕士研究生. 2017年获得南京航空航天大学工学学士学位. 主要研究方向为嵌入式系统, 导航系统, 多传感器数据处理. 本文通信作者.E-mail: wangyazhao001@163.com

赵伟：南京航空航天大学自动化学院副教授. 2002年获得南京航空航天大学导航、制导与控制博士学位. 主要研究方向为卫星导航与惯性组合导航, 信息融合, GPS应用.E-mail: zhwac@nuaa.edu.cn

徐海洋：南京航空航天大学自动化学院硕士研究生. 2019年获得南京航空航天大学工学学士学位. 主要研究方向为导航与控制.E-mail: xuhaiyang@nuaa.edu.cn

刘建业：南京航空航天大学自动化学院教授. 1995年获得南京航空航天大学导航、制导与控制博士学位. 主要研究方向为导航与控制, 惯性导航与组合导航, 测控系统.E-mail: ljyac@nuaa.edu.cn

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出版历程
- 收稿日期: 2019-06-04
- 录用日期: 2019-12-30
- 网络出版日期: 2021-10-27
- 刊出日期: 2021-12-23

Multiple Navigation Sensor Fault Diagnose Research Based on Multi-stage Attention Mechanism

1.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

Funds: Supported by National Natural Science Foundation of China (61533008, 61374115, 61603181), Foundamental Research Funds for the Central Universities (NS2018021), and Priority Academic Program Development of Jiangsu Higher Education Institutions

More Information

Author Bio:
WANG Ya-Zhao　Master student at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He receiced his bachelor degree from Nanjing University of Aeronautics and Astronautics in 2017. His research interest covers embedded system, navigation system, and multi-sensor data processing. Corresponding author of this paper.)

ZHAO Wei　Associate professor at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He received his Ph.D. degree in navigation, guidance and control from Nanjing University of Aeronautics and Astronautics in 2002. His research interest covers satellite navigation and inertial integrated navigation, information fusion, and GPS applications

XU Hai-Yang　Master student at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He receiced his bachelor degree from Nanjing University of Aeronautics and Astronautics in 2019. His research interest covers navigation and control

LIU Jian-Ye　Professor at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He received his Ph.D. degree in navigation, guidance and control from Nanjing University of Aeronautics and Astronautics in 1995. His research interest covers navigation and control, inertial navigation and integrated navigation, and measurement and control systems

摘要

摘要: 导航传感器在使用过程中容易发生故障, 针对传统方法对其间歇性和渐变性故障识别率低的问题提出了一种基于多阶段注意力机制的多传感器故障识别算法. 该算法采用基于长短期记忆神经网络和注意力机制的编码器−解码器结构, 根据多类导航传感器数据之间的空间相关性和时间相关性来进行多传感器的故障互判. 经验证, 该算法对多种类传感器的故障识别率高达97.5%, 可以高效地实现故障的检测和分类. 该方法可以准确识别出故障传感器和故障类型, 具有很强的工程应用价值.
- 多阶段注意力机制 /
- 长短期记忆神经网络 /
- 编码器−解码器 /
- 多类传感器 /
- 故障互判
Abstract: Since navigation sensors may malfunction in use, a multi-sensor fault diagnosis algorithm based on multi-stage attention mechanism is proposed to solve the problem of low diagnosis rate of intermittent defect and gradual fault. An encoder-decoder structure based on the long short term memory (LSTM) neural network and attention mechanism is adopted in the algorithm, and fault mutual diagnosis between multiple navigation sensors is based on spatial and time correlation between the data of multiple navigation sensors. It is verified that the fault diagnosis rate of the algorithm for multi-type sensors is as high as 97.5%. Besides, sensor faults can be detected and classified effectively by this algorithm. This method which has a strong engineering application value can accurately identify the fault sensor and fault type.
- Multi-stage attention mechanism /
- long short term memory neural network /
- encoder-decoder /
- multi-class sensors /
- fault mutual judgement

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图 1 多阶段注意力结构图

Fig. 1 Multi-stage attention structure

下载: 全尺寸图片幻灯片

图 2 局部注意力机制

Fig. 2 Local attention mechanism

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图 3 全局注意力机制

Fig. 3 Global attention mechanism

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图 4 时间注意力机制

Fig. 4 Time attention mechanism

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图 5 传感器数据状态图

Fig. 5 Status of sensor data

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图 6 传感器数据状态图

Fig. 6 Status of sensor data

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表 1 不同模型的实验对比

Table 1 Comparison of experimental results of different models

方法准确率 (%) 召回率 (%)

FDRNN 97.5 96.6
FDRNN-N1 83.4 81.5
FDRNN-N2 81.2 79.4
FDRNN-N3 74.3 72.1
DBN 80.5 78.9
DCNN 84.7 83.6

下载: 导出CSV

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