Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles

Li, Haoqian; Wang, Yong; Zeng, Jing; Li, Fansong; Yang, Zhenhuan; Mei, Guiming; Ye, Yunguang

doi:10.1016/j.ress.2024.110087

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Although this method has achieved high accuracy in detecting wheelset tread defects, it still faces practical challenges. Issues such as image blurring caused by vibration and unstable image frames due to high speed remain key areas for future research [32].…”

Section: Discussionmentioning

confidence: 99%

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Liu,

Jiang,

Wang

et al. 2024

Electronics

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Due to the long-term service through wheel-rail rolling contact, the train wheelset tread will inevitably suffer from different types of defects, such as wear, cracks, and scratches. The effective detection of wheelset tread defects can provide critical support for the operation and maintenance of trains. In this paper, a new method based on a local inference constraint network is proposed to detect wheelset tread defects, and the main purpose is to address the issue of insufficient feature spaces caused by small samples. First, a generative adversarial network is applied to generate diverse samples with semantic consistency. An attention mechanism module is introduced into the feature extraction network to increase the importance of defect features. Then, the residual spine network for local input decisions is constructed to establish an association between sample features and defect types. Furthermore, the network’s activation function is improved to obtain higher learning speed and accuracy with fewer parameters. Finally, the validity and feasibility of the proposed method are verified using experimental data.

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Section: Discussionmentioning

confidence: 99%

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Liu,

Jiang,

Wang

et al. 2024

Electronics

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“…Xie et al [ 10 ] developed a vehicle-track coupled dynamics model to simulate the dynamic response of the axle box under different speeds and track wear excitations. The sensor-based method has low environmental requirements and a small cost [ 11 , 12 , 13 ]. However, it may not be sensitive enough to detect subtle faults in some cases (e.g., surface cracks or minor wear), and it also affects the accuracy of detection in complex environments, such as when there is a large amount of noise interference.…”

Section: Introductionmentioning

confidence: 99%

RSDNet: A New Multiscale Rail Surface Defect Detection Model

Du,

Zhang,

Gao

et al. 2024

Sensors

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The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network’s attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

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State monitoring of brake uneven wear in high-speed train based on multi-source feature fusion

Zhang,

Xu,

et al. 2024

Structural Health Monitoring

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In the actual operation of high-speed trains, brake friction blocks work in a normal state at most times. In other words, most of the collected datasets on the uneven wear states are highly imbalanced. To address the issue of imbalanced data, a monitoring model based on a multi-head self-attention mechanism and a one-dimensional multi-scale convolutional neural network (MHSA–1DMCNN) is proposed, taking into account the correlation between multi-sources of data. First, multi-source friction interface parameters, such as vibration acceleration, braking noise, and friction coefficient, are collected as sample data to characterize the state of brake friction blocks. The Smote-Tomek method is used for comprehensive sampling of multi-source data. Then, a multi-head self-attention (MHSA) mechanism is utilized to extract important global information from multiple different representation subspaces. And 1DMCNNs are applied to extract friction block state features from multiple scales. Finally, MHSA is constructed to achieve the multi-source feature fusion for monitoring the uneven wear state. The experimental results show that the proposed model can maintain high recognition accuracy under different degrees of imbalance ratios. This provides a new feasible method for monitoring the uneven wear state of high-speed train brake pads under imbalanced data.

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Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles

Cited by 3 publications

References 27 publications

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

Detection of Train Wheelset Tread Defects with Small Samples Based on Local Inference Constraint Network

RSDNet: A New Multiscale Rail Surface Defect Detection Model

State monitoring of brake uneven wear in high-speed train based on multi-source feature fusion

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