Life Prediction of Railway Relays Based on TPE-Informer Model

Aiming at the problem of how to predict the remaining life of railway relay accurately and efficiently, a life prediction method of railway relay based on TPE-Informer model is proposed. Firstly, the electric life test platform of railway relay is built to obtain the degradation data of its whole life cycle, and the characteristic parameters that can reflect its operation state are extracted from it. Secondly, the extracted feature parameters are input into the Informer model for training, and the multi head sparse self-attention mechanism is used to mine the correlation between the states before and after the feature information. Finally, the tree-structured parzen estimator(TPE) is used to optimize the hyper parameters of Informer model to obtain better prediction performance. The experimental platform is used to collect data to verify the model, and the results are compared with the other three deep learning algorithms. The experimental results show that the proposed prediction model has higher prediction accuracy than RNN, LSTM and Informer models, with an average accuracy of 96.52%, small error rate and good stability, which proves the feasibility of the application of the prediction model.