Simulation Analysis and Prediction Model of Aircraft Electrostatic Discharge Based on Machine Learning

Based on machine learning method, the discharge law of aircraft deposition static electricity was studied in this paper. Some continuous influencing variables such as speed, height, time, temperature and humidity were selected to form a sample group of deposition static electricity influencing factors through simulation. Data samples of the deposited static electricity were obtained by the method of simulation, and the normalized operation was carried out by rationally selecting independent variables and dependent variables. We carried out the minimum redundancy maximum correlation (MRMR) technique to rank the importance of factors affecting the amount of deposited electrostatic field. Based on the ranking of the importance of the influencing factors of the deposition electrostatic, the corresponding dependent variables were simulated and generated to form the sample set of machine learning, and the prediction model of the deposition electrostatic discharge law was established based on the method of machine learning. The research results of this paper can provide theoretical support for the control of deposition electrostatic discharge, and also have certain engineering value.