Research on the Full Life Cycle Management and Intelligent Detection and Early Warning System of PHM Technology for EMU Wheel Sets Based on Big Data Analysis

To improve the effectiveness of fault prediction and health management for high-speed train wheels, this paper proposes a data processing framework based on Spark Streaming and Kafka to collect, clean, and transform high-speed train PHM source data.Based on the data processing, correlation algorithms were used to identify the influencing factors of wheel set wear.Considering the complexity of changes in high-speed train wheel size data influenced by operational environments and other factors, a wheel size prediction model based on VMD-PSO-MKELM was constructed to achieve accurate prediction of high-speed train wheel set data.In wheel diameter data, the MSE, MAE, and MAPE of the VMD-PSO-MKELM model in this paper are 0.0012, 0.0294, and 0.0004%, respectively, with R2 reaching 0.9968; For flange thickness data, the corresponding values are 0.0081, 0.0741, and 0.0005% for MSE, MAE, and MAPE, respectively, with an R of 0.9251.Whether in wheel diameter data or flange thickness data, the MSE, MAE, and MAPE of the VMD-PSO-MKELM model in this paper are lower than those of the compared ELM, L-ELM, P-ELM, and R-ELM models, and the R is the highest, demonstrating high prediction accuracy and greater practicality.