Bingrong Miao

Wheel flats deteriorate normal wheel-rail contact conditions and induce periodic impacts, thereby affecting the operational stability and reliability of railway vehicles. In practical engineering applications, the number of fault samples available for diagnosis is usually limited. Moreover, under variable-speed conditions, fault-related time-frequency characteristics in vehicle vibration responses vary significantly with operating speed, which further increases the difficulty of constructing labeled training datasets. To address these issues, a physics-conditioned denoising diffusion probabilistic model (DDPM)-based data augmentation method is proposed in this study. Impact occurrence timing and peak amplitude are extracted from vibration responses as response-derived physical conditions to characterize the temporal distribution of wheel-rail impacts and the variation in impact intensity. Subsequently, the physical conditions are transformed into two-dimensional conditional feature maps and introduced into the diffusion generation process, enabling the generation model to learn the evolution patterns of fault-related time-frequency characteristics caused by operating-condition variations. The proposed method is validated using a vehicle-track dynamic simulation dataset and a scaled vehicle-track test rig dataset. Compared with the original datasets, the proposed method improves the diagnosis accuracy from 87.8% to 94.4% on the simulation dataset and from 69.2% to 81.2% on the scaled vehicle-track test rig dataset. These results indicate that the proposed physics-conditioned DDPM can generate samples that better preserve impact-related time-frequency characteristics and improve wheel flat diagnosis performance under limited fault samples.

1. Wheel tread defects can directly lead to performance degradation and increased damage to railway vehicles and infrastructure, thereby increasing the cost of operation and maintenance of vehicles...

To solve the problems with the traditional Variational Mode Decomposition (VMD) method applied in Structural Health Monitoring (SHM), such as parameter sensitivity and high complexity, which can lead to low efficiency and accuracy in data feature extraction. A novel fault diagnosis method is proposed for axle box bearings. Firstly, the Morlet Wavelet combined with a sliding window approach is used to determine the inherent period of the data and define the range of the bearing fault feature data. Secondly, an improved VMD method based on cosine distance is introduced to adaptively determine the optimal number of decomposition modes of fault features. This approach effectively addresses the parameter sensitivity issue and enhances feature extraction efficiency. Thirdly, the Uniform Manifold Approximation and Projection (UMAP) method is used to compress the processed data to achieve online bearing monitoring and reduce data processing scale. Finally, the Convolutional Neural Networks (CNN) method is used to diagnose and classify the fault features to validate the efficiency of the proposed method. The experiment results indicate that the proposed approach yields a significant improvement in bearing fault diagnosis accuracy compared with other methods. Under noise-free conditions, the proposed method achieves an accuracy of approximately 98% across different datasets, which is notably higher than that of other diagnostic methods.

The accurate identification of abnormal impact loads generated by under-vehicle equipment is recognized as critical for assessing structural integrity in high-speed trains. This inverse problem is addressed through a novel load identification approach, which leverages sparse regularization principles. The precision of impact load identification is enhanced through a weighted l ₁ -norm regularization framework, aimed at mitigating solution ill-posedness and minimizing peak force errors. This framework is efficiently solved by an optimized TwIST algorithm, into which an iterative reweighting technique is incorporated. Furthermore, an improved AIC is employed for optimal regularization parameter selection, and an analysis of sample size selection is complemented. The method proved accurate and robust, as verified by comprehensive simulations and scaled carbody experiments. The proposed method is demonstrated to significantly outperform classical l ₁ -norm regularization in identifying impact loads, with superior accuracy and increased robustness exhibited with respect to measurement point selection and noise contamination.

High-speed railway is a comprehensive carrier of high-technology. As an important infrastructure of high - speed rail tracks, rail breakage and derailment accidents caused by rail rolling contact fatigue crack damage are quite common. Considering that a single eddy current pulsed thermography cannot directly and very accurately make a quantitative estimate of the geometric parameters of Rolling Contact Fatigue (RCF), this paper proposes a rail contact fatigue crack damage identification system and method based on the fusion of 2D optical images and eddy current thermography. A mathematical - physical model of Poisson reconstruction degree for the fusion of 2D optical images and eddy current thermography is proposed to quantitatively characterize the geometric parameters of rail cracks, such as length and depth. A rail contact fatigue crack damage identification robot system based on the fusion of 2D optical images and eddy current thermography is designed and built. Experiments are carried out to compare the detection accuracy of crack geometric parameter identification in static and dynamic (different motion states) modes, as well as to study the impact on the quantification of crack geometric parameters. The experimental results show that the proposed Poisson reconstruction degree based on the fusion of 2D optical images and eddy current thermography has good robustness in quantifying crack geometric parameters in the slow - speed mode, verifying the effectiveness of the system and method.

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future.