基于GWO-CNN-LSTM的铁路轨道高低不平顺值反演模型研究OA北大核心

In order to use the acceleration of the train body vibration to accurately inverse the track longitudinal irregularity value of railroads,this paper proposes a model(GWO-CNN-LSTM)based on Gray Wolf Optimization algorithm(GWO),Convolutional Neural Network(CNN)and Long Short Term Memory Network(LSTM)to construct the relationship between vehicle body vibration acceleration and track longitudinal irregularity value.Firstly,the measured data of the track inspection vehicle are pre-processed according to the characteristics of the data using the PauTa criterion for outlier rejection,and then the processed data are used with the vehicle vibration acceleration as the input of the model in which the track longitudinal irregularity is the output.The CNN is used to learn the waveform information of the vehicle body vibration acceleration,and the features learned by the CNN are input to the LSTM.Finally the key parameters of the LSTM model is optimized with GWO,then the inverse perform of the track longitudinal irregularity value is made.To highlight the adaptability of the model,additional zones are randomly selected for inversion.Three performance metrics are used to evaluate the model and compare with other classical methods.The results show that the root mean square error,the mean absolute error and the goodness of fit of the GWO-CNN-LSTM model are 0.141,0.107 and 0.977 respectively,and the GWO-CNN combined with LSTM can improve the goodness of fit by 20.1％compared with a single LSTM;compared with recurrent neural network,BP neural network and support vector regression,the proposed GWO-CNN-LSTM model has 68.1％～79.1％lower root mean square error,60.4％～68.3％lower mean absolute error,and 27.7％～44.9％higher goodness of fit,which verifies the GWO-CNN-LSTM model for inversion of track longitudinal irregularity values validity.The model provides a new idea for the study of railroad track longitudinal irregularity value inversion.