Xiaopei Cai

Existing studies have mainly focused on vehicle suspension control and coupled analysis of normal conducting maglev systems, while research on parameter optimization and sensitivity analysis of guideway girders for superconducting electrodynamic suspension (EDS) maglev systems on bridges remains limited. In this study, a coupled mechanical simulation model of the maglev vehicle, guideway, and bridge is developed. Considering vertical train loads, temperature rise of trackside coils, and high-speed operating conditions, the displacement, stress, dynamic amplification factor, vehicle vibration response, and levitation gap variation of the guideway girder are analyzed. The results show that increasing the girder height can improve the first-order vertical natural frequency of the structure and reduce the dynamic amplification effect. A girder height of 3.0–3.2 m provides relatively stable dynamic responses at 600 km/h. Under the structural form and calculation conditions considered in this study, a span of 24 m shows favorable dynamic response and engineering applicability. The concrete elastic modulus of the guideway girder is a key parameter affecting the system dynamic response. The levitation gap variation is sensitive to car body mass, and the air spring stiffness should be optimized by balancing vibration reduction performance and gap stability. The results can provide a reference for structural design and parameter optimization of guideway girders for superconducting EDS maglev systems on bridges.

Subgrade differential settlement can compromise ride safety and operational stability by intensifying wheel–rail interaction and vehicle–track vibrations. Early-grade identification remains challenging due to weak onset response variations and the nonstationarity of acceleration signals. Conventional data-driven models often provide neither physics-consistent indicators nor well-calibrated confidence for threshold-based alarms and risk grading. Physics-Aware Time-Series Mixer with feature-wise linear modulation priors (PATS) is developed for ordinal identification of settlement amplitude A, wavelength L, and lateral coefficient K using carbody lateral and vertical accelerations. PATS integrates (1) improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to separate low-frequency geometric trends from mid-to-high-frequency impacts, (2) physics-informed descriptors, namely sensitive intrinsic mode function (IMF) energy priors and statistics-based cumulative characteristic function (SCCF) features, injected into a lightweight Time-Series Mixer (TSMixer) backbone via feature-wise linear modulation (FiLM), and (3) a multi-group ordinal head based on consistent rank logits (CORAL) to output reliability-aware probabilities. Experiments show 97.41% accuracy with 0.0187 expected calibration error (ECE) on K at 1.050 ms/sample, 96.30% accuracy with 0.0350 ECE on A at 0.964 ms/sample, and 100.00% accuracy with 0.0011 ECE on L. The artificial intelligence contribution lies in an interpretable physics-guided prior injection mechanism for nonstationary multivariate time-series grading, coupled with a reliability-oriented ordinal output design. The engineering application contribution is a deployable onboard monitoring framework for early-grade identification of high-speed railway subgrade differential settlement from onboard accelerations, enabling threshold-based alarming and risk grading.

The failure to accurately identify abnormal vibrations in protected metro areas is a serious threat to the operational safety of metro tunnels and trains, and there is currently no suitable method for effectively improving the accuracy of abnormal vibration identification. To address this issue, an accurate method for identifying abnormal vibrations in a metro reserve based on spatially correlated dense measurement points is proposed. First, by arranging distributed optical fibers along the longitudinal length of a tunnel, dynamic strain vibration signals are extracted via phase-sensitive optical time-domain reflectometry analysis, and analysis of variance (ANOVA) and Pearson correlation analysis are used to jointly downscale the dynamic strain features. On this basis, a spatial correlation between the calculated values of the features of the target measurement points to be updated and its adjacent measurement points is constructed, and the spatial correlation credibility of the dynamic strain features between the dense measurement points and the target measurement points to be updated is calculated via quadratic function weighting and kernel density estimation methods. The weights are calculated, and the eigenvalues of the target measurement points are updated on the basis of the correlation credibility weights between the adjacent measurement points. Finally, a support vector machine (SVM) and back propagation (BP) identification model for the eigenvalues of the target measurement points are constructed to identify the dynamic strain eigenvalues of the abnormal vibrations in the underground tunnel. Numerical simulations and an experiment in an actual tunnel verify the effectiveness of the proposed method.

Turnout irregularity significantly affects the stochastic vibration behavior of vehicle–turnout structures. This study proposes a fitting formula for the turnout irregularity spectrum and develops a turnout irregularity full information expression model (TIFIEM) using a stochastic harmonic function. The model is applied to vehicle–turnout structure stochastic vibration and reliability analysis. Findings suggest that the Hamming window method, with a window length of 4096 points, is optimal for estimating the turnout irregularity spectrum. It is recommended to fit the power spectral density (PSD) using a 5th-order polynomial for better accuracy. The TIFIEM effectively addresses randomness in amplitude, frequency, and phase. An analysis of 250 irregularity samples is sufficient for the desired accuracy. Additionally, the PSD amplitude at various frequency points follows a Chi-square distribution with 2° of freedom. Regions 3–7 m from the tip of the switch rail on the straight switch rail and 53–54 m on the point rail are most susceptible to wear. When the vehicle passes through the turnout at 300 km/h, the reliability of vehicle–turnout structures at the crossing panel decreases to 95.8%.

Real-time monitoring and analysis of sensitive areas in high-speed railway (HSR) are crucial for ensuring the safe and smooth operation of high-speed trains. To address the problem of frequent missing and false alarms caused by anomaly data in HSR monitoring system, this study proposes an innovative network framework: Intelligent detection network of HSR anomaly monitoring data (HSRA-Net). The framework comprises of two modules: the data augmentation module and the anomaly detection module. The data augmentation module designs multiple alternative generative adversarial networks for sample augmentation. To achieve the end-to-end classification, the anomaly detection module improves the residual network by creating a deep residual shrinkage network with self-attention (DRSN-SA). An online monitoring system was installed and operated continuously for several years on a high-speed turnout of a continuous beam bridge to validate the proposed framework. The collected data includes displacement, stress, and temperature. The proposed framework has demonstrated excellent performance, generalizability, and deployability through sufficient model comparison. It can accurately and efficiently diagnose anomalies in the operation of the monitoring system. This study is of great significance for improving the anomaly detection task of the HSR monitoring system.