As the train speeds up, the damage caused by the collisions between trains and foreign objects are becoming more and more severe. Therefore, it is of great significance to monitor the intrusion of foreign objects in the track environment. In this paper, transfer learning is introduced into Mask-RCNN deep learning model. And the data set of rail image is used to train the model, which improves the effect of rail segmentation. The trained model is used to segment the rail in the picture, and the rail vulnerable area is divided based on the segmentation results. The sliding window ORB feature matching algorithm is used to calculate the similarity of vulnerable area. The detection of foreign objects in the area where is easy to invade is realized, and the detection reliability is improved. Experiments represent that this method has high accuracy, strong practicability, good robustness and universality.
Because the fault data of rail transit switch machine are difficult to obtain and the site fault is difficult to reproduce, it is difficult to diagnose or predict the switch machine. In this paper, the power fault data of S700K switch machine is divided into creeping fault and mutation fault, and a simulation data generator for generating massive fault data is developed. The generators involve the synthesis of minority over-sampling techniques and generative confrontation networks. Finally, the long-term memory neural network is used to predict the generated gradual fault data to verify the authenticity and reliability of the simulation data generator. The experimental results show that the generated data can predict the future power trend of the switch machine, which proves the authenticity and feasibility of the simulation data generator.
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