Analysis And Research On The Resonance Signal Of Middle And Low Speed Maglev Train

The medium and low-speed maglev train has many advantages such as fast speed increase,low noise,comfortable and safe ride,and low construction and maintenance costs.After years of development,it has become an important trend in the development of urban public transportation in the future.my country’s low-and medium-speed magnetic levitation technology has also achieved brilliant results.It has fully owned independent intellectual property rights and has successfully moved towards commercial operation.Maglev train technology is still in a period of rapid development.Because its stable suspension relies on electromagnetic force,the complex and changeable operating lines have brought many effects to its stable operation.Among them,the coupling resonance problem is particularly important.Maglev experiments in different countries Different degrees of vehicle-rail coupled vibration have occurred on the line,and under certain circumstances,bridges,rails,and vehicles may also have coupled vibrations at the same time.The resonance problem greatly affects the passenger’s riding experience.To a certain extent,the suspension system may lose stability.In order to make the maglev train safer and more reliable,it is necessary to conduct continuous analysis and research on the resonance problem of the train.Therefore,from a different perspective,the classification,identification and prediction of the resonance of the maglev train is of great significance to the stability of the levitation control system and the safe operation of the maglev train.This thesis first describes the development of maglev train technology and the research background and significance of maglev train resonance.Through the analysis of the maglev train suspension control system,it is proposed to analyze and analyze the resonance problem of the maglev train from the perspective of data processing.Research;because the support vector machine algorithm has great advantages in solving problems such as small samples and non-linearity,this thesis uses the algorithm to classify and identify the two main types of coupling resonance that currently exist in the low-and medium-speed maglev train;secondly,because The parameter optimization of the support vector machine model has a huge impact on the simulation results.This thesis uses the particle swarm algorithm to optimize the model parameters and proposes improvement measures to further improve the classification accuracy.The simulation experiment has achieved good classification results.Thirdly,in order to know the existence of resonance point in advance,and timely feedback to the suspension system and the driver to take corresponding measures,this thesis uses the time domain threshold method to numerically determine the occurrence of resonance,namely the resonance coefficient,which is used as the factor of the prediction model.Variables;in order to improve data availability and prediction accuracy,time-domain feature parameters and empirical mode decomposition are introduced to preprocess the collected data,rebuild feature vector data as independent variables,and then also use particle swarm optimization and its improved algorithm to The parameters of the prediction model are optimized.Through simulation experiments,it is found that the prediction model optimized by the improved particle swarm algorithm has the lowest prediction error of the resonance coefficient and has good prediction performance.Finally,the comprehensive experimental results show that from the point of view of data processing,the support vector machine algorithm is used to analyze and study the resonance problem of the maglev train,which has achieved good classification and prediction results,and provided great solutions for subsequent resonance problems help.