基于数据驱动的无人机集群故障检测与诊断

李润泽; 姜斌; 余自权; 陆宁云

doi:10.13700/j.bh.1001-5965.2022.0441

基于数据驱动的无人机集群故障检测与诊断

doi: 10.13700/j.bh.1001-5965.2022.0441

南京航空航天大学自动化学院，南京 211100

基金项目: 国家自然科学基金 (62020106003,61663008)；直升机动力学全国重点实验室开放基金(2024-ZSJ-LB-02-04)

详细信息

通讯作者:
E-mail：binjiang@nuaa.edu.cn

中图分类号: V279；V241
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出版历程
- 收稿日期: 2022-05-31
- 录用日期: 2022-06-17
- 网络出版日期: 2022-11-19
- 整期出版日期: 2024-05-29

Data-driven fault detection and diagnosis for UAV swarms

College of Automation Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 211100，China

Funds: National Natural Science Foundation of China (62020106003,61663008); Open fund of the National Key Laboratory of Helicopter Aeromechanics (2024-ZSJ-LB-02-04)

More Information

Corresponding author: E-mail：binjiang@nuaa.edu.cn

摘要

摘要:
无人机集群系统对安全性和稳定性要求极高,实时的故障检测与诊断技术是保证安全可靠运行的有力手段。提出一种基于统计模型和改进宽度学习(BLS)模型的故障诊断方法。所提方法通过多元数据统计分析表征无人机集群系统在正常与不同故障模式下的行为特征，采用改进的BLS模型实现准确、快速的故障诊断。在此基础上，开发无人机集群系统的高逼真可视化仿真验证平台对所提方法的合理性与有效性进行验证。实验结果表明，所提方法与目前主流方法相比具有明显的诊断优势。
- 无人机集群 /
- 故障诊断 /
- 统计模型 /
- 宽度学习 /
- 仿真验证
Abstract:
The security requirements of the unmanned aerial vehicle (UAV) swarm system are extremely strict, and real-time fault detection and diagnosis is one of its important supporting technologies. This paper presents a fault diagnosis method based on statistical model and improved broad learning system (BLS) model. Firstly, the behavior characteristics of UAV swarm system under normal and different fault modes are characterized by multivariate data statistical analysis, and then the improved BLS model is used to achieve accurate and rapid fault diagnosis. On this basis, a high fidelity simulation verification platform is developed to verify the rationality and effectiveness of the proposed method. The experimental results show that the proposed method has obvious diagnostic advantages compared with the current mainstream methods.
- UAV swarm /
- fault diagnosis /
- statistical model /
- broad learning system /
- simulation verification

HTML全文

图 1 本文提出的故障检测与诊断框架流程

Figure 1. Illustration of fault detection and diagnosis scheme of this paper

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图 2 BLS网络架构

Figure 2. Architecture of broad learning system

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图 3 多旋翼无人机非线性故障注入模型^[20]

Figure 3. Nonlinear fault injection model of multi-rotor UAV^[20]

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图 4 故障注入仿真

Figure 4. Fault injection simulation

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图 5 软件在环仿真平台的故障检测结果

Figure 5. Fault detection results of software-in-the-loop simulation platform

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图 6 传统BLS和改进BLS参数确定

Figure 6. Parameters determination of original BLS method and improved BLS method

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图 7 4种情况下使用传统BLS和改进BLS的诊断结果

Figure 7. Fault diagnosis result of original BLS method and improved BLS method in four conditions

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表 1 4种情况下不同方法的诊断结果对比

Table 1. Diagnosis comparison of different methods under four conditions

方法诊断准确率/%

SAE 76.8

SVM 93.8

BLS 96.8

本文方法 99.2

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