JACIII Vol.25 No.2 pp. 195-203
doi: 10.20965/jaciii.2021.p0195


Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

Changfan Zhang, Hongrun Chen, Jing He, and Haonan Yang

College of Electrical and Information Engineering, Hunan University of Technology
No.89 Taishan Xi Road, Tianyuan District, Zhuzhou, Hunan 412007, China

Corresponding author

October 10, 2020
December 21, 2020
March 20, 2021
high-speed train, generative adversarial network, data dimensionality ascending, convolutional neural network, reconstruction accuracy
Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

Reconstruction frame of high-speed train measurement data based on WGAN

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

Cite this article as:
Changfan Zhang, Hongrun Chen, Jing He, and Haonan Yang, “Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.2, pp. 195-203, 2021.
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Last updated on Apr. 13, 2021