State Evaluation of Wheel–Rail Force in High-Speed Railway Turnouts Based on Multivariate Analysis and Unsupervised Clustering

The assessment of wheel–rail force states is a key technical issue in the safety monitoring of high-speed railway turnouts. Due to the complex geometry and severe load fluctuations of turnouts, wheel–rail interactions exhibit strong nonlinearity, asymmetry, and multidimensional coupling characteristics. Traditional methods suffer from limitations such as reliance on labeled samples and poor real-time performance. This study proposes an intelligent evaluation method that integrates multivariate statistical analysis with unsupervised clustering, and establishes a multidimensional analytical framework incorporating data preprocessing, time-domain analysis, safety index evaluation, frequency-domain feature extraction, and cluster-based recognition. Using a turnout section of the Beijing–Tianjin Intercity Railway as a case study, four fundamental wheel–rail force components were selected as feature variables to reveal their dynamic patterns. The DBSCAN density-based clustering algorithm was employed to achieve unsupervised state identification, successfully classifying three typical operating states: normal, high-load abnormal, and extreme load. The clustering silhouette coefficient reached 0.563, significantly outperforming K-means and hierarchical clustering. Safety evaluation results indicate that all relevant indicators meet international standards. The proposed method requires no labeled samples and offers strong physical interpretability and engineering applicability, providing effective support for turnout condition awareness and predictive maintenance.