Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation threshold for defects detection. Two visual comparison experiments were carried out to demonstrate the efficiency of the proposed methods. Finally, a quantitative comparison experiment shows the LWLC-GSME model achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects. Therefore, LWLC for image enhancement, in conjunction with GSME for defects segmentation, is efficient and feasible for the detection of rail surface defects based on UAV Images.

Traction converters are safety-critical parts of traction drive systems on high-speed trains. Considering the complicated interconnections in the traction converter, it is imperative to utilize multi-sensors rather than single-sensor for fault diagnosis. However, the multi-sensor-based traction converter fault diagnosis is barely discussed by now. Concerning this issue, this paper proposes a novel intelligent diagnosis framework based on Long Short-term Memory network, which deals with extracting long-term dependencies in time series effectively and learns hidden fault characteristics from traction converter multi-sensor signals adaptively, without needs of expert knowledge or system modeling. Experiments were designed to verify the model's sensitivity on different signals as single-sensor respectively, and as multi-sensors integrally. Nine kinds of measured fault signals from actual High-speed Trains in operation were utilized for verification. Results show that the proposed method has the highest sensitivity to different signals compared to conventional models, and the crucial fault modes were identified accurately, which implies the high reliability of multi-fault identification. In addition, experiments on different scales of training sets also suggest that the model accuracy can be improved by increasing the training data scale appropriately. Besides, this method can be further applied to other train systems with similar data characteristics in the future.

The height and stagger of the contact wire directly affect the energy supply of high-speed trains. To ensure the operation safety, there is an urgent demand for high-speed railways to measure the static parameters of contact wires all over the line with high precision and efficiency. However, this issue is barely discussed. Concerning the issue, this paper proposes a UAV-LiDAR-based measuring framework for the static height and stagger of high-speed railway contact wire. By mounting LiDAR on the UAV, the framework can efficiently collect data from the lines in service without occupying the train operating-diagrams. It is extremely significant for the high-speed and high-density railways. Then, we present self-adaptive extraction algorithms to extract critical infrastructures (rails, contact wires, masts and other suspensions) based on their specific geometric characteristics as well as the continuity and consistency of the spatial distributions along the line. Finally, the height and stagger are calculated by formulas automatically. To verify the framework in practice, we tested it on Beijing-Shanghai high-speed railway, which is the busiest high-speed railway in China. It is shown that the measurement error is within 9mm and the framework has potential to reform the inspection of high-speed railways.

Turnouts are crucial to the safety of high-speed railways. Due to the intensive use and complex environment, breakdowns caused by different faults occur frequently in practice. Considering that the operation of turnouts is a multi-stage process during which each stage has its specific health characteristics, this paper proposes segmentalized maximal-relevancy and minimal-redundancy (mRMR) for feature extraction from each stage separately. Based on mathematical analysis of the turnout mechanism, the electric power curve is segmented into four stages, from which time-domain analysis and mRMR are combined to extract valid features corresponding to different movements respectively. Then, a novel classifier named cost-sensitive Extreme Learning Machine with fixed inputs (cf-ELM) is proposed for fault classification. We modify the inputs of ELM and define a new formula to limit the input weights and biases for the sake of stability of the network structure. Besides, a cost-sensitive optimization method is also presented in this classifier to embed the failure degree and data proportion into cost calculation rules to deal with data imbalance. To verify our proposed method, real data collected from a turnout of Beijing-Shanghai high-speed railway is used. It is proven by comparisons that the accuracy of our method has achieved 100% with fast running speed and also outperforms traditional methods in terms of stability and generalization remarkably.

Point cloud semantic segmentation (PCSS) is crucial for digital twins of high-speed railways. By now, the concerned subjects are confined within the interior infrastructures of railways. However, the surrounding environments are also important for the safe operation. Concerning this issue, a full-range high-speed railway scanning scheme based on unmanned-aerial-vehicle-borne LiDAR is utilized. However, the massive data volume and data distribution imbalance pose great challenges for PCSS. To address these issues, a novel PCSS framework called 3DGraphSeg is proposed in this article. To cope with the massive data volume, a structural representation algorithm named local embedding super-point graph is proposed to represent the vast point cloud into a concise graph while retain the data's inherent topology structure by local spatial embedding. Then, the gated integration graph convolutional network (GIGCN) is proposed to contextual segment the graph. In the GIGCN, to prevent the gradients from vanishing or exploding, the hidden states of gated recurrent units in every layer are integrated using a new layer named gated hidden states integration (GHSI). GHSI strengthens the back propagation by giving the loss function direct access to each layer and absorbs the features of different layers comprehensively, which enables the network to produce a smoother decision boundary and prevents the overfitting problem. Besides, to enhance its robustness to data imbalance, we propose a loss function: adaptive weighted cross entropy. Finally, five experiments are designed for verification. The proposed framework has excelled in different datasets and outperforms state-of-the-art approaches on the SemanticRail dataset.