The Prediction of the Motor State of the Shore Bridge Based on the Generative Resistance Network GAN

2018 2nd International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Shi, C.^1 ; Tang, G.^1 ; Li, Y.^1 ; Hu, X.^1

Logistics Engineering College, Shanghai Maritime University, 1550 Harbor Avenue, Shanghai, China^1

关键词: Accurate prediction;    Driving system performance;    Driving systems;    Historical data;    Internal faults;    Prediction model;    Resistance network;    Vibration characteristics;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/435/1/012010/pdf
DOI  :  10.1088/1757-899X/435/1/012010

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In order to forecast the trend and the vibration characteristic parameters of the shore bridge driving system and find the internal faults. This paper analyze the trend by using a large number of bridge lifting motor historical data based on the theory of generative information against network GAN, introduce the base theory of the generative theory against network in detail, establish a prediction model based on GAN and use the model to predict crane driving system performance parameters. The experimental results show that the prediction model based on GAN can achieve satisfactory results in the prediction and realize the accurate prediction of the change trend of the bridge driving system.