Research on Fault Detection Methods of High-Speed Railway Passenger Transportation Management System Using Autoencoder and Variational Autoencoder

The high-speed rail passenger transportation management system, as an integral part of modern railway transportation, bears the crucial responsibility of facilitating people's travel. However, with the continuous advancement of high-speed rail technology and the expansion of operational scale, the complexity of high-speed rail systems is also increasing. Consequently, the frequency and impact of system failures are attracting growing attention. This study aims to explore the methods of using autoencoders and variational autoencoders for fault detection in high-speed rail passenger transportation management systems. Through the analysis of experimental results, it was found that variational autoencoders have certain advantages in handling high-dimensional data and complex features, enabling better modeling of data distribution and enhancing the accuracy and robustness of fault detection. The study also revealed that the performance of autoencoders and variational autoencoders is influenced by multiple factors, including model architecture, hyperparameter settings, and data partitioning methods. By comparing different data partitioning methods and parameter selections and evaluating and analyzing their performance, areas for further improvement and optimization were identified, such as adopting more advanced network architectures and improving anomaly detection algorithms. Through the exploration and practical application of this research, a deeper understanding of the methods of using autoencoders and variational autoencoders for fault detection in high-speed rail passenger transportation management systems has been achieved, providing valuable insights and guidance for enhancing the safety and reliability of high-speed rail passenger transportation systems.