A Comprehensive Evaluation Method for the Medium- and Low-Speed Maglev Trains Suspension System Based on Gaussian Mixture Model

Maglev trains, as an emerging transportation modality, have attracted significant attention with respect to their safety and ride comfort. In this study, the improved R index and τ-distance index are incorporated into the evaluation framework, and a data-driven comprehensive evaluation method for the suspension system of medium- and low-speed maglev trains is developed based on a Gaussian mixture model, enabling a comprehensive assessment of suspension gap stability and operational smoothness. Experimental results demonstrate that the proposed method can accurately identify various motion modes of the suspension system and provide effective early warnings of abnormal operational states. Compared with conventional error integral performance indices, this method exhibits superior anomaly detection sensitivity and enhanced interpretability of the results. Computational efficiency analysis indicates that the proposed method meets the requirements for online real-time monitoring. Under different operating conditions, the GMM trained on normal operational data maintains stable evaluation performance, demonstrating favorable robustness.