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基于生成对抗网络的航天异常事件检测方法

张克明 蔡远文 任元

张克明, 蔡远文, 任元等 . 基于生成对抗网络的航天异常事件检测方法[J]. 北京航空航天大学学报, 2019, 45(7): 1329-1336. doi: 10.13700/j.bh.1001-5965.2018.0682
引用本文: 张克明, 蔡远文, 任元等 . 基于生成对抗网络的航天异常事件检测方法[J]. 北京航空航天大学学报, 2019, 45(7): 1329-1336. doi: 10.13700/j.bh.1001-5965.2018.0682
ZHANG Keming, CAI Yuanwen, REN Yuanet al. Space anomaly events detection approach based on generative adversarial nets[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1329-1336. doi: 10.13700/j.bh.1001-5965.2018.0682(in Chinese)
Citation: ZHANG Keming, CAI Yuanwen, REN Yuanet al. Space anomaly events detection approach based on generative adversarial nets[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(7): 1329-1336. doi: 10.13700/j.bh.1001-5965.2018.0682(in Chinese)

基于生成对抗网络的航天异常事件检测方法

doi: 10.13700/j.bh.1001-5965.2018.0682
基金项目: 

国家自然科学基金 51475472

国家自然科学基金 61803383

国家自然科学基金 51605489

详细信息
    作者简介:

    张克明  男, 博士研究生, 高级工程师。主要研究方向:异常事件检测、信号识别

    蔡远文  男, 博士, 教授。主要研究方向:航天器测试与发射

    任元  男, 博士, 副教授。主要研究方向:导航、制导与控制

    通讯作者:

    任元, E-mail: renyuan_823@aliyun.com

  • 中图分类号: TP391.4

Space anomaly events detection approach based on generative adversarial nets

Funds: 

National Natural Science Foundation of China 51475472

National Natural Science Foundation of China 61803383

National Natural Science Foundation of China 51605489

More Information
  • 摘要:

    航天环境复杂,技术难度大,风险高,安全可靠性要求苛刻。航天异常事件样本少,且难以获取,有针对性地开展异常事件检测(AED)很有必要。为预防航天事故,尽早发现可能导致故障的异常事件,深入研究了最新人工智能和生成对抗网络(GAN)技术,提出了一种基于生成对抗网络的航天异常事件检测方法。使用正生成对抗网络模拟生成正常事件样本,训练反生成对抗网络模拟生成异常事件样本,设计合理算法训练测试,计算输入事件与正生成对抗网络生成的模拟正常事件欧氏距离,以及输入事件与反生成对抗网络生成的模拟异常事件的欧氏距离差,实现对异常事件的精确检测。通过在美国国家标准与技术研究所数据库(MNIST)数据集全部使用正常数据训练,并对异常事件检测性能进行了试验验证,试验结果表明:在MNIST数据集下,精确率和召回率综合评价指标(F1)及精确率和召回率曲线下面积(PRC)等关键技术指标比变分自动编码器(VAE)方法相应指标性能至少分别提升了31%和11%。在真实环境下采集的模拟航天音频数据试验,异常事件检测性能良好,进一步证实了所提方法真实可用。

     

  • 图 1  生成对抗网络结构示意图

    Figure 1.  Schematic diagram of structure of generative adversarial nets

    图 2  航天异常事件检测结构组成

    Figure 2.  Structure constitution of space anomaly events detection

    表  1  基于MNIST数据集异常检测性能

    Table  1.   Anomaly detection performance on MNIST

    异常数字 F1 精确率 虚警率 AUC
    0 0.746 0.745 0.329 0.878
    1 0.701 0.548 0.430 0.758
    2 0.783 0.712 0.357 0.552
    3 0.779 0.712 0.362 0.531
    4 0.787 0.716 0.351 0.499
    5 0.801 0.705 0.331 0.616
    6 0.786 0.786 0.353 0.699
    7 0.775 0.523 0.368 0.271
    8 0.787 0.772 0.351 0.618
    9 0.784 0.680 0.356 0.463
    下载: 导出CSV

    表  2  本文方法与VAE方法异常检测性能对比

    Table  2.   Anomaly detection performance comparison between proposed method and VAE method

    异常
    数字
    本文方法 VAE[12]
    F1 PRC F1 PRC
    0 0.746 0.745 0.537 0.517
    1 0.701 0.548 0.205 0.063
    2 0.783 0.712 0.598 0.644
    3 0.779 0.716 0.332 0.251
    4 0.787 0.705 0.381 0.337
    5 0.801 0.786 0.427 0.325
    6 0.786 0.846 0.433 0.432
    7 0.775 0.522 0.212 0.148
    8 0.787 0.772 0.490 0.499
    9 0.784 0.679 0.210 0.104
    下载: 导出CSV

    表  3  真实环境数据检测性能

    Table  3.   Data detection performance in real environment

    迭代次数 F1 精确率 召回率 AUC
    1 0.778 0.747 0.813 0.617
    2 0.963 0.940 0.987 0.978
    3 0.981 0.987 0.975 0.984
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-22
  • 录用日期:  2019-02-16
  • 刊出日期:  2019-07-20

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