A state detection method for metro track irregularity based on hybrid deep learning model

Track irregularity evaluation is crucial for ensuring vehicle stability, ride comfort, and safe operation. In the actual operation of the line, it is essential to study the relationship between the vibration state of the metro vehicle and the track irregularity to evaluate the service state of the track and guide the maintenance and repair of the line. To accurately evaluate the irregularity of the metro track, this paper establishes a hybrid deep learning model, which consists of a convolutional neural network, a long short-term memory network, and an improved sand cat swarm optimization algorithm. The model uses vehicle acceleration and running speed as inputs to achieve end-to-end prediction of track irregularity. The hyperparameters of the convolutional neural network and long short-term memory are obtained by the improved sand cat swarm optimization algorithm. Four evaluation indices are adopted to analyze the effectiveness of the presented model and contrast it with other classical models. The hybrid deep learning model is verified using data from comprehensive inspections of metro line vehicles in a city. The experimental results show that the improved sand cat swarm optimization algorithm performs far better than other methods, which can increase the convergence accuracy and speed of the test function and help obtain the optimal hyperparameter configuration of the model. Compared to the traditional models, the hybrid deep learning model can effectively reduce the error, and the max goodness of fit reaches 0.952, which verifies the effectiveness of the model for track irregularity identification. The research results offer a novel approach to studying track irregularity state recognition.