An aircraft fuel data missing value filling method with generative adversarial network

doi:10.3785/j.issn.1008-9497.2021.04.002

Journal of Zhejiang University (Science Edition)

2021, Vol. 48

Issue (4): 402-409 DOI: 10.3785/j.issn.1008-9497.2021.04.002

Data Visual Analysis and Vitual Reality

An aircraft fuel data missing value filling method with generative adversarial network

GUO Yibo¹, NIU Meng¹, WANG Haidi¹, CHEN Yanhua¹, XUE Junxiao¹, YUAN Yue¹, HOU Lishuo¹, XU Mingliang¹, PAN Jun²

1.Information Engineering College, Zhengzhou University, Zhengzhou 450001, China
2.China Aviation Industry Corporation Jincheng Nanjing Electromechanical Hydraulic Engineering Research Center, Nanjing 211106, China

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Abstract The fuel data collected by aircraft sensors is the basis for various follow-up studies on aircraft. However, due to various factors, the data collected by sensors always have many missing values. The direct use of such data for subsequent processing will lead to a decrease in the accuracy of the algorithm. Therefore, the effect of processing missing values in the data is crucial. The existing missing value filling algorithm has two problems. On the one hand, it ignores the historical dependence of time series in the time dimension; on the other hand, some algorithms require a complete data set for model training. In order to solve the above problems, this paper uses a method of filling missing values based on adversarial networks. This method can effectively solve the problems of traditional methods, and can achieve the best filling effect compared with other algorithms.

Key words： generative adversarial networks Seq2seq model recurrent neural network missing value filling method

Received: 07 December 2020 Published: 25 July 2021

CLC:

TP 391.4

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	GUO Yibo
	NIU Meng
	WANG Haidi
	CHEN Yanhua
	XUE Junxiao
	YUAN Yue
	HOU Lishuo
	XU Mingliang
	PAN Jun

Cite this article:

GUO Yibo, NIU Meng, WANG Haidi, CHEN Yanhua, XUE Junxiao, YUAN Yue, HOU Lishuo, XU Mingliang, PAN Jun. An aircraft fuel data missing value filling method with generative adversarial network. Journal of Zhejiang University (Science Edition), 2021, 48(4): 402-409.

URL:

https://www.zjujournals.com/sci/EN/Y2021/V48/I4/402

基于生成对抗网络的飞机燃油数据缺失值填充方法

飞机传感器采集的燃油数据是后续燃油测量的基础。在飞行过程中，由传感器采集的数据因存在部分缺失值，如直接进行后续处理将影响燃油测量精度。现有的缺失值填充方法存在两方面问题，一方面易忽视飞机燃油时序数据在时间维度上的上下文依赖关系；另一方面缺少完整的样本数据集进行模型训练。基于此，提出了一种基于生成对抗网络的缺失值填充方法，从而有效解决了传统方法难以处理的时序数据历史隐含规律及样本不完整的问题，且填充效果较其他算法更佳。

关键词： 循环神经网络, 生成对抗网络, 缺失值填充方法, Seq2seq 模型

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