Identification Algorithm of Rail Transit Pantograph-catenary Contact Force Abnormality Based on LSTM Prediction Error

Abstract

Objective Contact force is a crucial aspect of comprehensive detection in the rail transit PC (pantograph-catenary) system and serves as an important evaluation factor of PC system performance. However, during the detection process, the external environment factors often lead to abnormal detection data. Currently, the elimination of abnormal PC contact force detection data mainly relies on manual methods, which affect data analysis efficiency. Therefore, there is a need to conduct in-depth research on an identification algorithm for PC contact force abnormality in PC system. Method The common abnormal forms of PC contact force are categorized, and the characteristics of contact force detection data under different abnormal conditions are analyzed. An identification algorithm for PC contact force abnormality based on LSTM (long short-term memory) prediction error is proposed. Using normal contact force data to train an LSTM model enables it to predict the trend of contact force variations. To achieve precise differentiation between normal segments and abnormal points, an abnormal data detection method based on the confidence interval is used. To mitigate the impact of long-distance abnormal data on LSTM model prediction performance, a prediction value replacement method for handling abnormal data is proposed. The effectiveness of identifying three common abnormal forms of PC contact force is verified using real detection data obtained from high-speed comprehensive inspection trains. Result & Conclusion Research results show that the proposed algorithm can effectively identify abnormalities in the PC contact force.

Keywords

• rail transit
• identification algorithm of pantograph-catenary contact force abnormality
• lstm prediction error