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| Flight parameter data anomaly detection method based on improved generative adversarial network |
Peng ZHANG1( ),Zi-du TIAN2(),Hao WANG1 |
1. Engineering Training Center, Civil Aviation University of China, Tianjin 300300, China
2. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China |
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Abstract Deep learning and generative adversarial network technology were analyzed aiming at the stringent safety and reliability requirements of civil aircrafts, the scarcity of abnormal samples of flight parameter data in actual civil aviation operations, the imbalance of the overall sample and the lack of labeling. A flight-level anomaly detection method for flight parameter data was proposed based on improved generative adversarial network. The method does not rely on the number of samples and labels, and realizes the detection method of unsupervised learning. Normal data samples were input for flight parameter data. The easy-to-converge WGAN-GP improved generative adversarial network model was applied to simulate normal data samples. The Bhattacharyya distance between the input data and the simulated normal data was calculated, and the detection of abnormal data was realized. The test verification was conducted through the artificial synthesis data set of NASA simulated flight parameter data and the flight parameter data set constructed by the quick access recorder data collected in the real operating environment. Results show that the proposed method has a significant improvement in some anomaly detection performance indicators compared with the commonly used unsupervised model.
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Received: 23 October 2021
Published: 25 October 2022
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| Fund: 国家自然科学基金-民航联合基金资助项目（U1733201） |
基于改进生成对抗网络的飞参数据异常检测方法
针对民用飞行器安全性、可靠性要求严苛，实际民航运营中飞行参数的异常样本稀少，整体样本不平衡且缺少标注的问题，研究深度学习与生成对抗网络技术，提出基于改进生成对抗网络的飞参数据飞行级异常检测方法. 该方法不依赖样本数量与标签，实现无监督学习的检测方法. 针对飞参数据，输入正常数据样本，应用易收敛的WGAN-GP改进型生成对抗网络模型，模拟生成正常数据样本，计算输入数据与模拟正常数据的巴氏距离，实现对异常数据的检测. 通过美国国家航空航天局模拟飞参数据的人工合成数据集以及真实运营环境下采集的快速存取记录器数据构建的飞参数据集，开展试验验证. 结果表明，与常用无监督模型相比，提出方法在部分异常检测性能指标上有显著提升.
关键词：
异常检测,
生成对抗网络,
飞参数据,
机器学习,
无监督学习
|
|
| [1] |
CHANDOLA V, BANERJEE A, KUMAR V Anomaly detection: a survey[J]. ACM Computing Surveys, 2009, 41 (3): 1- 58
|
|
|
| [2] |
ZHAO W, LI L, ALAM S, et al An incremental clustering method for anomaly detection in flight data[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103406
doi: 10.1016/j.trc.2021.103406
|
|
|
| [3] |
彭宇, 何永福, 王少军, 等 飞行数据异常检测技术综述[J]. 仪器仪表学报, 2019, 40 (3): 1- 13
PENG Yu, HE Yong-fu, WANG Shao-jun, et al Flight data anomaly detection: a survey[J]. Chinese Journal of Scientific Instrument, 2019, 40 (3): 1- 13
|
|
|
| [4] |
孙文柱, 曲建岭, 袁涛, 等 基于改进SVDD的飞参数据新异检测方法[J]. 仪器仪表学报, 2014, 35 (4): 932- 939
SUN Wen-zhu, QU Jian-ling, YUAN Tao, et al Flight data novelty detection method based on improved SVDD[J]. Chinese Journal of Scientific Instrument, 2014, 35 (4): 932- 939
|
|
|
| [5] |
LI L, DAS S, JOHN HANSMAN R, et al Analysis of flight data using clustering techniques for detecting abnormal operations[J]. Journal of Aerospace Information Systems, 2015, 12 (9): 587- 598
doi: 10.2514/1.I010329
|
|
|
| [6] |
MELNYK I, YADAV P, STEINBACH M, et al. Detection of precursors to aviation safety incidents due to human factors [C]// 13th IEEE International Conference on Data Mining Workshops. Dallas: IEEE, 2013: 407-412.
|
|
|
| [7] |
MELNYK I, BANERJEE A, MATTHEWS B, et al. Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 1065-1074.
|
|
|
| [8] |
BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: identifying density-based local outliers [C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2000: 93-104.
|
|
|
| [9] |
GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al Generative adversarial nets[J]. Communications of the ACM, 2020, 63 (11): 139- 144
doi: 10.1145/3422622
|
|
|
| [10] |
SCHLEGL T, SEEBÖCK P, WALDSTEIN S M, et al. Unsupervised anomaly detection with generative adversarial networks to guide Marker discovery [C]// International Conference on Information Processing in Medical Imaging. Cham: Springer, 2017: 146-157.
|
|
|
| [11] |
ZENATI H, ROMAIN M, FOO C S, et al. Adversarially learned anomaly detection[C]// 2018 IEEE 18th International Conference on Data Mining Workshops. Singapore: IEEE, 2018: 727-736.
|
|
|
| [12] |
YU J, LIN Z, YANG J, et al. Free-form image inpainting with gated convolution [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 4471-4480.
|
|
|
| [13] |
陈雪云, 黄小巧, 谢丽 基于多尺度条件生成对抗网络血细胞图像分类检测方法[J]. 浙江大学学报: 工学版, 2021, 55 (9): 1772- 1781
CHEN Xue-yun, HUANG Xiao-qiao, XIE Li Classification and detection method of blood cells images based on multi-scale conditional generative adversarial network[J]. Journal of Zhejiang University: Engineering Science, 2021, 55 (9): 1772- 1781
|
|
|
| [14] |
XIA B, BAI Y, YIN J, et al LogGAN: a log-level generative adversarial network for anomaly detection using permutation event modeling[J]. Information Systems Frontiers, 2021, 23 (2): 285- 298
doi: 10.1007/s10796-020-10026-3
|
|
|
| [15] |
张克明, 蔡远文, 任元 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
ZHANG Ke-ming, CAI Yuan-wen, REN Yuan Space anomaly events detection approach based on generative adversarial nets[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (7): 1329- 1336
|
|
|
| [16] |
ARJOVSKY M, BOTTOU L. Towards principled methods for training generative adversarial networks [EB/OL]. (2017-01-17)[2022-09-29]. https://arxiv.org/pdf/1701.04862.pdf.
|
|
|
| [17] |
ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks [C]// Proceedings of the 34th International Conference on Machine Learning. Sydney: PMLR, 2017: 214-223.
|
|
|
| [18] |
RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks [C]// 4th International Conference on Learning Representations. San Juan: [s. n. ], 2016.
|
|
|
| [19] |
GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANS [C]// Advances in Neural Information Processing Systems. Long Beach: Curran Associates, 2017: 30.
|
|
|
| [20] |
DONAHUE J, KRÄHENBÜHL P, DARRELL T. Adversarial feature learning [C]// 5th International Conference on Learning Representations. Toulon: [s. n.], 2017.
|
|
|
| [21] |
DAS S, MATTHEWS B L, SRIVASTAVA A N, et al. Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study [C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010: 47-56.
|
|
|
| [22] |
ZHAO Y, NASRULLAH Z, LI Z Pyod: a python toolbox for scalable outlier detection[J]. Journal of Machine Learning Research, 2019, 20 (5): 1- 7
|
|
|
| [23] |
RASMUSSEN C E. Gaussian processes in machine learning [C]// Summer School on Machine Learning. Berlin: Springer, 2003: 63-71.
|
|
|
| [24] |
VAN DER MAATEN L, HINTON G Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9 (11): 2579- 2605
|
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