The vibration state of rail vehicles in actual line operation is important information for evaluating the stability,ride comfort and safe operation of vehicles.However,it is difficult to measure the real-time vibration state of rail vehicles due to the variable operating conditions.To solve this challenge,a new method of vehicle vibration state identification based on Bayesian optimization algorithm (BO),convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed.The measured vibration-related data are preprocessed by normalization.The lateral velocity and yaw velocity of the bogie in the processed data are used as inputs to the model,and the lateral acceleration and yaw acceleration of the vehicle body are taken as outputs of the model.The CNN is utilized to learn and extract the waveform characteristics of lateral velocity and yaw velocity of the bogie,and the corresponding characteristics are input into LSTM.Meanwhile,the BO optimization is adopted to obtain the optimal hyperparameter configuration of the LSTM model.Then,the lateral acceleration and yaw acceleration of the vehicle body are output to achieve the real-time state identification of the lateral vibration state of the rail vehicle body.Considering the characteristics of the actual operating conditions of rail vehicles,three performance indicators are applied to analyze the effectiveness of the model.Moreover,it is compared with other classical methods.The results show that compared with the traditional BP neural network and CNN-LSTM model,the real-time identification of the lateral vibration state of the vehicle body through the BO-CNN-LSTM model can effectively reduce the error,attain the highest accuracy and exhibit spectral density waveforms closest to the true values.In the face of operating conditions such as different vehicle speeds and loads,the average absolute error and root mean square error of the model are below 0.019 and 0.029 respectively.Additionally,the coefficient of determination is above 0.99.This verifies the effectiveness of the BO-CNN-LSTM model for the lateral vibration state identification of the vehicle body.The research results provide new perspectives for the real-time identification of the operational state of rail vehicles.
维普
万方数据