Rail fasteners are crucial railroad infrastructure and their health status is directly connected to the safety of traveling trains. UAV-based rail fastener visual inspection has shown strong advantages over traditional inspection techniques. However, existing general-purpose object detection architectures inevitably suffer from the limitation that they can only detect objects that are clearly visible. Consequently, they tend to neglect objects affected by occlusion or shadows and lead to missed detection problem. The industrial application of such models can bring huge safety risks to long-term operations of safety-sensitive railroad systems. Concerning the issues, this paper proposes a visual prior-guided rail fastener integrity detection architecture (RFIDet) to realize coarse-to-fine detection of all rail fasteners, whether normally visible or visually obscured. RFIDet employs a two-stage pipeline: the visual prior guidance (VPG) stage generates standard rail fastener layout representation (SRFLR) for coarse priors, while the precise location search (PLS) stage enables NMS-free refinement using adaptive anchors designed from actual physical distance priors. SRFLR takes full advantage of inherent spatial priors of all rail fasteners to perceive a unified, interconnected, and coarse location distribution. Then all rail fastener candidates activated by those coarse locations are further trained to search and regress refined offsets to the final bounding boxes. Structural loss functions for both stages are customized to facilitate the detection of individual fasteners while constraining the overall spatial distribution of all fasteners. Experiments have verified the effectiveness and better robustness of the proposed RFIDet with the mAP50value increased by at least 5.9% compared to a series of general-purpose SOTA YOLO detectors. RFIDet outperforms the comparing algorithms especially when coming across unexpected occlusions or shadows.

The significance of intelligent fault diagnosis techniques is increasing in maintaining the security and reliability of railway operations. In particular, few-shot learning shows promise since it can address the issue of limited fault samples. However, the existing approaches have the following shortcomings. First, they ignore rich fault information in the joint distributions of multi-dimensional features, limiting the improvement of diagnosis accuracy. Second, they lack specialized mechanisms to alleviate severe degradation in diagnosis accuracy caused by label mistakes in engineering scenarios. To address the above issues, a Brownian distance covariance-based few-shot learning framework of fault diagnosis considering noisy labels is proposed for train transmission systems. In network construction, a novel joint distribution expression (JDE) layer is developed and embedded into the prototypical network, implementing similarity measures based on the joint distribution characteristics. In network training, a new antimislabeling learning strategy is designed for few-shot fault diagnosis tasks, in which a similarity-weighted prototype aggregation mechanism mitigates the negative effects of mislabeled support samples and a loss function with outlier attenuation avoids disruptions from mislabeled query samples. Taking a fault diagnosis study case of motors, gearboxes, and axle boxes as examples, it is demonstrated that the proposed framework can effectively learn and recognize faults, even using limited training datasets containing parts of mislabeled samples. Moreover, the superiority of the proposed framework is demonstrated by comparing it with other state-of-the-art methods.

Point cloud-based 3D object detection technology provides precise information for detecting obstacles in front of trains. In rail transit scenarios, obstacles are usually located at a distance, and the sparsity of point cloud data leads to information loss during feature extraction and spatial transformation, adversely affecting the accuracy of obstacle detection. To tackle this issue, we propose RAE3D, a single-stage end-to-end architecture that incorporates a multiscale voxel feature aggregation module on point queries (MVA-PQ) and a bird's-eye view multilevel auxiliary (BEV-MLA) module for efficient 3D object detection. The MVA-PQ module encodes multiscale voxel features into centroids of the final voxel layer, integrating spatial and scale information. The BEV-MLA module converts shallow 3D features into sparse 2D features using height compression, guiding the network to learn the spatial structure of small objects. We also introduce a 3D centroid offset loss for optimizing bounding boxes. Extensive experiments on the Rail3D and KITTI datasets have demonstrated the superiority of the proposed RAE3D.

Railway object intrusion poses a significant threat to railway safety, so it is vital to monitor the obstacles within the track area in real-time to prevent accidents, which can be achieved by vision-based technologies. However, most existing vision-based railway obstacle detection algorithms are limited to stationary cameras, which restricts the monitoring range. In contrast, onboard approaches enable full-line monitoring but face more challenges in achieving high accuracy and real-time performance. To address these challenges, we developed an onboard end-to-end multitask perception model (MTP-Rail), which includes variants of different scales (S, M, L) to realize railway obstacle detection in real-time with high accuracy. Our model consists of two decoders, which can simultaneously implement the tasks of object of interest detection and track segmentation. In addition, we designed a post-processing scheme to analyze the relationship between the detected object and the track and assess the obstacle risk level. Experiments on our dataset show that one of the proposed model MTP-rail-M achieved an 89.6% classification accuracy of obstacle risk level and 54.3% on mAP, 78.6% on mPA and 64.9% on mIoU with an inference speed of 181 FPS at an input size of$640\times 640$on RTX 3060Ti. Our model is easy to install and deploy, highlighting its potential in engineering applications. The code is available onhttps://github.com/ccl-1/obstacle_detection.

Regular inspection of potential risks in railroad surroundings is essential for operational safety. Uncrewed aerial vehicles (UAVs) offer an effective solution with aerial mobility and long-distance coverage. However, existing methods struggle with rare but extremely high risks characterized by limited samples and complex feature distributions. To address this, we propose SRLF (Sparse Representation Learning Framework), which decomposes sparse risks (SR) perception into three components: capture, excavation, and learning. First, Buffer Decouple Learning (BDL) decouples objectness from classification to capture and enhance foreground perception. Second, Feature Space Dynamic Sampling (FSDS) leverages adaptive quantity sampling from multivariate Gaussian distributions to excavate discriminative SR representations. Third, Triple Similarity Loss (TSL) constructs a triple comparison mechanism to contrastively shape uncertainty surfaces between SRs and common safety hazards (CSHs). Finally, extensive experiments conducted on the UAV-based railroad surroundings dataset demonstrate that SRLF can achieve a high detection rate of CSHs (95.6% mAP) while maintaining low miss-detection rate for SRs (81.9% Recall and 0.5% FPR95).

The metro train localization in an underground environment is a challenging task to provide the reliable and accurate train location for railway operation and maintenance. Nowadays, a user-friendly, high-precision, cost-effective, and convenient train localization method is needed for onboard railway inspection in the underground environment. In this research, we propose a train localization method by train motion and track geometry features fusion. A single microelectromechanical system (MEMS) inertial measurement unit (IMU) mounted onboard an in-service train, is used to sense the train motion, and the measurements are mapped into absolute location along the track by map-matching. Based on that, the train speed estimation is calibrated through train motion and track geometry features fusion to update the train location on the track. Then, the average localization error is estimated to evaluate the train localization performance in the absence of the real train trajectory data. The field experiment is carried out to verify the applicability and assess the train localization accuracy of the proposed method, the results of which show less than 1% average localization errors in an interval. Data from MEMS IMUs mounted on different cars are also tested, showing satisfactory repeatability and reliability performance.

Rapid movement of the wheels on defective tracks and high-frequency friction collisions cause vibration between train and rail track, which greatly damages the lifespan of train components and is a significant cause of train derailments. Timely and accurate detection of rail abrasions is of great significance for ensuring the safety of railway operations. Deep learning based-automatic rail abrasion detection methods face the challenge of having fewer samples. Thus, a deep transfer learning framework based on improved YOLOv8 models is developed to detect rail abrasions to address the issue of few-shot learning. Then, deformable convolution networks (DCNs), convolutional block attention module (CBAM) and a new intersection over union (IoU) loss are introduced to improve the detection performance. The ablation experiments show that it effectively reduces the rate of missed and false abrasion detections. By comparing with the existing detection methods, the developed YOLOv8DTL method has higher precision, recall, and average precision under different abrasion size thresholds, indicating that it is more adaptable to detection tasks with different abrasion sizes. It also has the best robustness, maintaining a high level of detection efficiency.

Potential safety hazards (PSHs) around the high-speed railroad need to be detected and evaluated regularly and timely to ensure high-speed railroad operation safety. Unmanned aerial vehicle (UAV)-based PSH evaluation has great potential to supplement the current manual visual inspection tasks by providing better overhead views and less man-made accidents. This study presents an evaluation system for PSHs along high-speed railroad tracks. First, a novel hybrid learning architecture named UYOLO (U-shape You Only Look Once) is designed, which integrate the CSP-based backbone and detection branch to produce three scale high-level features for the object detection. Then, an innovative parsing branch inserted behind high-level layer of the structure progressively transmits context information to the shallow layer to accurately accomplish the pixel-level parsing task. Second, a new loss function using minimum point distance IoU (MPD-IoU) is designed and incorporated into the architecture to optimize the coordinate regression process of the predicted bounding boxes. Conveniently, an image-based hazard evaluation model is also developed and integrated to rate the hazard level of the detected PSHs. Finally, extensive experiments conducted on the track environment dataset established with UAV imagery indicate the proposed system can achieve a high detection rate yet remains efficient and convenient.

Vibration data compression is crucial for addressing the considerable data volume challenge in prognostics and health management (PHM). This challenge can be mitigated by compressing both the number of sample points and the size of individual sample points. However, achieving high-compression ratios (CRs) encounters two primary challenges. First, current optimal solutions for compressing sample point sizes, data binarization, suffer from low-compression efficiency. Second, in hybrid compression, the compression effects of individual sample point sizes are prone to being lost during the reduction of sample points, thus limiting the improvement of CRs. To address these challenges, a novel hybrid compression framework is introduced for vibration condition monitoring. Building upon this framework, an efficient compression method with encryption features is proposed. The main contributions of the proposed method are twofold. First, by introducing the concept of clustering-based binarization, compression of sample point sizes with high CRs is achieved while improving compression efficiency. Second, by designing compression sampling methods that preserve the original data properties, the failure of individual sample point size compression is prevented, and compression space is further expanded while enhancing data security. Experimental results demonstrate the overall superiority of the proposed method. Compared to existing approaches, it achieves significant improvements in CR while retaining key spectral information, enhancing compression efficiency, and ensuring better data security. Thus, it alleviates the challenges of the significant data volume posed to data storage, transmission, and processing in PHM.

Continual learning is gaining special attention in remaining useful life (RUL) prediction of machinery recently, which enables deep prognostics networks to use incremental samples to progressively improve network performance without laborious retraining. Nonetheless, current studies exhibit several constraints: 1) An explicit mechanism is lacking in preventing the loss of pivotal memories after multiple continual learning stages. 2) A sampling-enhanced replay technique is lacking for continual learning-based RUL prediction. To address the abovementioned limitations, this article proposes a complementary continual learning framework for RUL prediction of machinery, which contains two novel characteristics, i.e., long-term potentiation and associative replay. These two characteristics are complementary and coenhanced. The long-term potentiation focuses on multistage continual learning, which is able to prevent deep prognostics networks from forgetting the formerly learned degradation knowledge. The associative replay pays attention to each new continual learning stage, which is able to consolidate typical degradation knowledge into new network learning. The proposed framework is verified using run-to-failure datasets from rolling element bearings, and the framework is also compared with some state-of-the-art methods. Experimental results indicate that the proposed framework can possess lower forgetting and achieve better prognostics performance reinforcement during continual learning.

Railway scene understanding is key to autonomous train operation and important in active train perception. However, most railway scene understanding methods focus on track extraction and ignore other components of railway scenes. Although several semantic segmentation algorithms are used to identify railway scenes, they are computationally expensive and slow with limits applications in railways. To solve these problems, we propose efficient dual-stream fusion network (EDFNet), a lightweight semantic segmentation algorithm, for understanding railway scenes. First, a dual-stream backbone network based on mobile inverted residual blocks is proposed to extract and fuse detailed features and semantic features. Next, a bi-directional feature pyramid pooling module is proposed to obtain multi-scale features and deep semantic features. Finally, a multi-task aggregate loss is designed to learn semantic and boundary information, thus improving the accuracy without increasing the computational complexity. Extensive experimental results demonstrate that EDFNet outperforms the lightweight state-of-the-art algorithms with high accuracy and fast speed on two railway datasets.

Railway intrusion seriously threatens railway safety and can cause enormous loss of life and property. Therefore, railway intrusion detection is crucial for the safety of railway operation. Among the current methods of intrusion detection, machine vision-based methods have been widely used in railways, and have attracted close attention because of their great benefits. This paper proposes a comprehensive review of railway intrusion detection based on machine vision, covering ground monitoring, on-board inspection, and unmanned aerial vehicle (UAV) inspection. First, this paper systematically reviews most of the studies over the past two decades and presents the survey in three parts. Second, by analyzing these studies and the requirements for railway monitoring, we summarize the major challenges that hinder railway intrusion detection based on machine vision. Finally, we propose several promising perspectives for railway intrusion detection based on machine vision by comprehensively considering the development of machine vision, sensors, and pattern recognition together with the needs of railway scenes.

UAVs have a broad application prospect in the field of railway inspection due to their excellent mobility and flexibility. However, it still faces challenges, such as high human labor costs and low intelligence levels. Therefore, it is of great significance to develop a real-time intelligent rail recognition algorithm that can be deployed on the onboard computing device to guide the UAV’s camera to follow the target rail area and complete the inspection automatically. However, a significant issue is that rails from the perspective of UAVs may appear with changing pixel widths and various inclination angles. Concerning the issue, a general and adaptive rail representation method based on projection length discrimination (RRM-PLD) is proposed. It can always select the optimal representation direction, horizontal or vertical, to represent any kind of rails. With the RRM-PLD, a novel architecture (Real-Time Rail Recognition Network, TriRNet) is proposed. In TriRNet, a designed inter-rail attention (IRA) mechanism is presented to fuse local features of single rails and global features of other rails to accurately discriminate the geometric distribution of all rails in the image in a regressive way and thus improve the final recognition accuracy. Further, one-to-one mapping from anchor points to final feature maps is established. It greatly simplifies the model design process and improves the model’s interpretability. Besides, detailed model training strategies are also presented. Extensive experiments have verified the effectiveness and superiority of the proposed formulation in terms of both network reasoning latency and recognition accuracy.

The health monitoring of traction motors is crucial for the prognostics and health management of high-speed trains. The temperature signal is an outstanding health indicator. Due to the representing of the traction motor's health conditions and low cost, accurate prediction for the motor temperature is conducive to early detection of abnormalities. However, the traditional prediction models are trained offline with high dependency on training data and cannot adapt to varying distributions of real data timely. Therefore, over time, the accuracies of these models always decrease noticeably. Concerning this issue, we propose an online health monitoring framework for traction motors using temperature signals. First, in the offline phase, multisensor signals are utilized to develop a generalized prediction model to absorb extensive information from temperature and relevant signals. Second, during the online phase, the training parameters are dynamically estimated to fulfill individualized learning by adopting a combination of the sample complexity and real-time prediction errors so as to fulfill individualized training according to the monitored data samples. Furthermore, a low-regret strategy is also presented in the online phase to determine the optimization target of the model to make the online update adaptive enough to the online prediction task. Consequently, the model can obtain new knowledge and greater understanding about the real data by online-learning continuously. Finally, the proposed framework is verified by actual data collected from Chinese high-speed trains. Compared with the conventional multilayer perceptron, gated recurrent unit, and long short-term memory, new patterns of stream data can be captured and adapted by using our framework, and the average root mean square errors of prediction results are reduced by 5%, 12%, and 11%, the average mean absolute percentage errors are reduced by 10%, 12%, and 11%, respectively. It is proven that our framework has high prediction accuracy and well-performed adaptability on real datasets.

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.

Noise barriers play a critical role in reducing noise and preventing foreign object from invading railway. Noise barrier structural defects such as rusted column, deteriorated mortar layer and other damages make its structure unstable, thereby threatening seriously railway operation safety. Unfortunately, existing noise barrier inspection methods still rely heavily on manual inspection, which are low-efficiency, subjective and difficult to detect the external structure of noise barriers. To solve these problems, this study proposes an automatic inspection manner for noise barrier using UAV images, and develops a fully convolutional network (FCN)-based noise barrier defect detection approach named skip connection YOLO detection network (SCYNet), which focuses on three aspects: network structure, loss function and data augmentation. First, a skip-connected feature structure Simi-BiFPN is incorporated into the network to fully fuse the features extracted from various scale layers without adding much computational overhead. Second, a NoiseIoU loss for bounding box regression is designed to improve existing IoU-based losses and get better performance on small dataset. Thirdly, a mixed sample data augmentation method named AutoFMix is proposed to eliminate the over-fitting issue caused by excessive similarity between samples, and further improve the detection accuracy. Finally, experiments conducted on the UAV railway noise barrier dataset show that the proposed SCYNet model achieves 92.2 mAP and 78.7 FPS, respectively, which outperform other models in terms of accuracy and processing speed. The fast-processing speed and high detection accuracy can quickly turn UAV images into useful information to assist railway maintenance, thereby improving the safety of train operation.

High-speed train bogies are the critical components of high-speed trains, which can play the role of traction, braking, and buffering. In long-term train service, bogies are prone to wear and aging. Currently, most studies on fault diagnosis methods are aimed at single equipment identification. It is challenging to accurately diagnose the faults of such coupled multi-equipment combination systems as bogies. Multiple devices on the bogie have implied correlations in space, and fully exploiting their spatial features enhances the fault diagnosis accuracy. This paper proposes a new bogie fault diagnosis method based on the directional graph of train bogie: RS-GAT model. The model uses the Residual-Squeeze Network (RS-Net) to construct the framework of the model and use the Graph Attention Network (GAT) for spatial information fusion and feature extraction to identify bogie faults. Using six datasets collected under the operation of high-speed trains, experimental results demonstrate that the proposed approach has better effectiveness than the RS-Net class model and single-layer graph class model, with diagnosis accuracy near 96%. Ablation experiments and comparisons between RS-GAT and RS-GCN verify the effectiveness of RS-Net framework and GAT model in fault classification. In addition, the RS-GAT model is found to have strong robustness when different models are analyzed using small-scale data sets.

Point cloud semantic segmentation for railway infrastructures is an essential step towards establishing railway digital twins. Deep learning-based methods have shown great potential in this field compared to traditional methods that rely on hand-crafted features. However, deep learning-based methods for railway point clouds still face typical challenges that need to be addressed. In this regard, we propose a novel learning framework named SALAProNet, which consists of a set of effective and concise modular solutions. The first challenge addressed is the massive data scale of railway point clouds, which makes it difficult to directly process large-scale point clouds due to memory limitations. To solve this problem, we adapt efficient random sampling in the network and propose the Self-Attentive Aggregation (SAA) module based on an attention mechanism to greatly expand the receptive field, which covers the unsampled points and successfully retains information in a high-dimensional feature space. The second challenge is fine-grained segmentation, where we propose the Local Geometry Embedding (LGE) module to embed local geometry. With the help of context information provided by SAA, the network can perform fine-grained segmentation for railway infrastructures. The third challenge is the insufficient generalization ability of the network, where we propose a Prototype Guided Regularization (PGR) method to guide the network to segment the point cloud among railways with different construction standards. This method enhances the network’s interpretability and improves its generalization ability. We have validated our proposed framework through experiments on different datasets, and it outperforms state-of-the-art approaches.

Newly deployed trains have massive normal data and scarce faulty data for training, which limits the diagnosis accuracy with class imbalance problem of small samples. Considering that there are a lot unutilized information hidden in the abundant unlabeled monitoring data, this paper proposes a novel method named manifold-contrastive broad learning system, which utilizes the online updating approach for dealing with the class imbalance problem of small samples. This method constructs a novel one-class broad-learning classifier based on an inherency-guided comparison mechanism, which can classify and annotate unlabeled data online. This classifier employs contrastive manifold matrices to maintain the inherent structures, which is not affected to the overfitting caused by imbalanced samples. Secondly, inspired by the active learning, this classifier proposes the minimum-error strategy to annotate the samples by classifying the modes, which solves the problem of insufficient training data. Thirdly, this method applies an incremental learning strategy that continuously absorbs the newly annotated data to update the model online, which improves the model accuracy under the data imbalanced condition. Finally, the feasibility and effectiveness of the proposed method are verified by wheelset bearing data collected from a test rig of a Chinese rolling stock company.

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.

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.

The multi-hop protocols are proved effective in the railway disaster wireless monitoring system. However, farther transmission distance with the larger data will decline the valid lifetime and reliability of the system. Most existing studies focused primarily on the communication protocols optimization, and some works tried to utilize the limited computation ability at the network-level or node-level, which are insufficient for the stiff disaster information monitoring demands. This paper presents an adaptive hybrid computation and communication strategy to fully taking advantage of the sensor processing ability, and improve the energy efficiency at the link-level. Furthermore, an adaptive optimization model is designed to meet the different monitoring demands of the system, and the valid lifetime is improved accordingly. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed protocol in the lifetime improvement, energy consumption minimization and equalization compared with other outstanding protocols.

The bogie system is a critical system for a high-speed train, which is composed of various mechanical parts. Therefore, the health of the bogie can directly affect the health of high-speed train. Temperature signals, speed signals and pressure signals are collected from the bogie can reflect its health. Hence, the multi-sensor fault diagnosis methods can provide novel solutions for the bogie health monitoring tool. This paper presents a novel bogie fault diagnosis scheme named the AttGGCN model, using graph convolutional network (GCN), gated recurrent unit (GRU) and attention mechanism. In this fault diagnosis scheme, the bogie data network is established firstly. Then, temporal and spatial features are extracted and fused using GCG unit. Finally, the GCN are used for fault identification. Twenty-four kinds of measured signals and seven types of faults from actual High-speed train in operation are utilized for verification. Results show that the AttGGCN model has the highest accuracy compared to conventional models. In addition, experiments on different scales of training sets suggest that the AttGGCN model has strong robustness in small-scale datasets. Besides, ablation experiments certificate that the attention mechanism is able to strengthen the feature extraction ability.

UAV-based automatic railway inspection is expected to have the potential to reform the inspection of railways. In this area, real-time railway scene parsing is quite essential. However, the limited computation resources of the UAV onboard computer pose a huge challenge for the algorithm to juggle a precise prediction with strong timeliness. Concerning this issue, this paper proposes a novel algorithm named deep fully decoupled residual convolutional network, which consists of fully decoupled residual blocks (Non-bottleneck-FDs) to deal with the dilemma between the high demand of real-time and limited resources. The residual block is constructed based on a new convolution which divides the standard convolution into three sequential convolutions to decouple the conventional operational correlations fully. Furthermore, a customized auxiliary line loss (LL) function is proposed to constrain the segmentation of railway and non-railway simultaneously without increasing the computation complexity. The proposed LL can force the predicted railway areas to concentrate in long strip areas precisely and inhibit their appearances in other impossible local areas. Subsequently, an integrated loss backpropagation strategy of the LL and cross-entropy function is presented. A comprehensive set of experiments are conducted for verification. Experiments demonstrate the superior performance of our approach with a more than$2\times $reduction in parameters and computation cost. Moreover, our approach also has a faster inference speed than the most existing lightweight architectures while providing comparable or higher accuracy. It is proven that our approach can reconcile the precise prediction with strong timeliness for railway scene parsing within the limitation of onboard computers. Besides, the results also imply its highest performance in terms of local details and edges of railway areas.

As an essential part in the rail train, the bogie plays an important role in the safety of the train operation. However, the fluctuant wheel-rail connection, as well as the structure and complex operating environment of the bogie always lead to low signal-to-noise ratio condition and complicated wheel-rail dynamic coupling relationship. The existing fault diagnosis methods can hardly perform well in this scenario. Concerning this issue, a novel feature extraction method named fully decomposed singular value (FdSV) is proposed in this paper. FdSV can decompose singular value characteristics of signals completely and increase the divergence of features to extract weak fault features effectively. Then, inspired by the theory of compressed perception and Hierarchy-ELM, a fixed dictionary extreme learning machine (FD-ELM) is also proposed for fault identification. This method calculates the weight matrix by formulas without randomization and removes the bias matrix. Therefore, it can easily discover the internal laws of data and improve the running speed and accuracy rapidly. Finally, the proposed algorithms have been verified by actual bogie data collected from bogies under low SNR and variable working conditions. Compared with SVD, the FdSV features are 1%-6% higher in testing accuracies. The accuracies of FD-ELM are 2-20% higher than the conventional ELM, H-ELM and SVM.

Accurate and real-time fault diagnosis (FD) and working conditions identification (WCI) are the key to ensuring the safe operation of mechanical systems. We observe that there is a close correlation between the fault condition and the working condition in the vibration signal. Most of the intelligent FD methods only learn some features from the vibration signals and then use them to identify fault categories. They ignore the impact of working conditions on the bearing system, and such a single-task learning method cannot learn the complementary information contained in multiple related tasks. Therefore, this article is devoted to mining richer and complementary globally shared features from vibration signals to complete the FD and WCI of rolling bearings at the same time. To this end, we propose a novel multitask attention convolutional neural network (MTA-CNN) that can automatically give feature-level attention to specific tasks. The MTA-CNN consists of a global feature shared network (GFS-network) for learning globally shared features and K task-specific networks with feature-level attention module (FLA-module). This architecture allows the FLA-module to automatically learn the features of specific tasks from globally shared features, thereby sharing information among different tasks. We evaluated our method on the wheelset bearing data set and motor bearing data set. The results show that our method has a better performance than the state-of-the-art deep learning methods and strongly prove that our multitask learning mechanism can improve the results of each task.

As one of the main pollution sources in China, haze can blur the railway monitoring and threaten railway safety. In this paper, we propose an end-to-end multi-scale residual network (MSRN) which can achieve remarkable dehazing effect on railway monitoring images. The method optimizes the image dehazing algorithm in three aspects: network structure, loss function and hazy dataset. Firstly, inspired by the residual network, the paper presents a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. Secondly, a combined loss function is designed to achieve better convergent results by balancing training time, training calculations, and precision. Thirdly, the paper synthesizes an outdoor dataset specifically for railway scenarios, which relies on real depth maps and various outdoor images. Extensive experimental results on both full reference image quality assessment and no reference image quality assessment of image restoration demonstrate that the proposed algorithm shows higher performance than the state-of-the-art algorithms. Moreover, the haze of railway monitoring images is removed under hazy weather, and the detection algorithm achieved higher detection accuracy on the images after dehazing. The proposed network structure, loss function, and hazy dataset are discussed and analyzed in detail to verify the effectiveness of the proposed method.

This paper investigates a design method for guidance signage systems in public spaces. A guidance graph method is proposed based on a combination of the shortest path algorithm and the proposed limited penetrable MAKLINK graph (LPMG) to determine the sets of guidance demand points and potential sign locations. The memory duration of pedestrians is measured in a cognitive experiment to determine the expected distance between guidance points. To reasonably estimate the coverage of guidance signs, a multifeature fusion-based interaction (MFI) model is proposed. Then, a binary linear programming formulation of a location-allocation model is proposed based on the MFI model. The proposed model can suggest the optimal orientations of signs in addition to their optimal number and locations. Finally, the effectiveness of the proposed method is illustrated through a real-world case study. The case study shows that the proposed model can produce a much more economical and pedestrian-friendly location-allocation plan than previous methods. A sensitivity analysis shows that the number of signs is a piecewise decreasing function with respect to the letter height and the expected distance between guidance points.

In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. To be specific, a passive WiFi sensing model is first established based on a probabilistic analysis of interactions between WiFi sniffers and the moving pedestrian flow, capturing the main factors affecting pedestrian flow characteristics. On that basis, a sequential filtering algorithm is proposed based on the Rao-Blackwellized particle filter (RBPF) to produce simultaneous and efficient estimates of the pedestrian flow speed and pedestrian number utilizing the real-time sniffing results. In order to validate this study, an experimental pedestrian surveillance system using WiFi sniffers is deployed at the transfer channel of a metro station in Guangzhou, China. Extensive experiments are conducted to verify the passive sensing model, and confirm the effectiveness and advantages of the proposed algorithm. The pedestrian flow estimation not only helps to improve the safety and facility management and customer services, but also paves the way for introducing other novel applications.

The catenary support device inspection is of crucial importance for ensuring safety and reliability of railway systems. At present, visual detection tasks of catenary support devices defect are performed by trained personnel based on the images taken periodically by industrial cameras installed on inspection vehicle in a limited period of time at midnight. However, the inspection mean is inappropriate for low efficiency and high cost. This paper presents a novel network based on unmanned aerial vehicle (UAV) images for catenary support device inspection and focuses on small object detection and the imbalanced dataset. With regards to the first aspect, based on a pyramid network structure, the improved Faster R-CNN consists of a top-down-top feature pyramid fusion structure, which heavily fuses high-level semantic information and low-level detail information. The feature map fusions of three different pooling scales are employed for improving detection accuracy of predicted bounding boxes. With regards to the second, we copy and paste the small proportion objects of dataset for avoiding category imbalance. Finally, quantitative and qualitative evaluations illustrate that the improved Faster-RCNN achieves better performance over the classic methods, yet remains convenient and efficient.

Energy efficiency of wireless sensors is critical to maintaining the function of the monitoring system. Generally, the energy consumed in data transmission is much larger than in compression. Hence, decreasing data packet size with the aid of data compression before transmission can facilitate the reduction of energy consumption in communication. However, the energy consumed in data computation is also considerable, and improper computation ways may incur more energy consumption. To address this issue, in this article, two-hierarchy communication and computation hybrid optimization protocol is presented to minimize the total energy consumption. First, the cluster heads (CHs) rotation and clusters updating strategies are proposed in the communication layer, and the optimized adaptive compression ratios for the CHs are adopted in the computation layer. The hybrid optimization scheme is performed from the views of communication and computation synergistically to improve energy efficiency. The simulation results show the superiority of the proposed protocol compared with other outstanding protocols.

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks. That is, how to understand and learn discriminant fault features from complex monitoring signals remains a great challenge. Considering this challenge, this article explores the use of the attention mechanism in fault diagnosis networks and designs attention module by fully considering characteristics of rolling bearing faults to enhance fault-related features and to ignore irrelevant features. Powered by the proposed attention mechanism, a multiattention one-dimensional convolutional neural network (MA1DCNN) is further proposed to diagnose wheelset bearing faults. The MA1DCNN can adaptively recalibrate features of each layer and can enhance the feature learning of fault impulses. Experimental results on the wheelset bearing dataset show that the proposed multiattention mechanism can significantly improve the discriminant feature representation, thus the MA1DCNN outperforms eight state-of-the-arts networks.

The multi-hop communication protocol can balance the energy consumption of sensors to extend the service lifetime in high-speed railways (HSRs). However, the communication via multiple hops will increase the data transmission latency. Most previous studies have focused on optimizing either the sensor network lifetime or the data transmission latency but have not considered both. This paper presents an adaptive multi-objective optimization model for multi-hop communication systems. This model explicitly addresses the trade-off between the lifetime and the latency associated with the use of network-level wireless condition monitoring systems for ensuring the railway operational safety. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed approach. Compared with the three previously applied protocols, the proposed approach can achieve longer sensor network lifetime, shorter data latency, and greater system utility (accounting for both lifetime and latency). This paper provides the technical support for the development of stable and reliable wireless monitoring management systems for HSR safety.

Bogie is one of the most major mechanical part of railway train. Its security and reliability are of paramount importance. Since research in this field is still on the early stage, which focus on either mechanical structure without condition or binary coherent systems. A multistate network flow model has been proposed in this paper with consideration of components degradation level and functional interaction between them. Firstly, the structure and function of the bogie for CRH3 were made a detailed introduction. Then transmission paths of three types force on bogie were study to determine the network strcture. Different from other papers, arcs represent the components and nodes are the transitive relation. Arc capacity tends to be confirmed easily with utilization of performance deterioration of elements on bogie involved in force tranferring. Flow rate of each arc depends on both component' health status and the task it undertakes. Furthermore, the minimal paths (MPs) method and the recursive sum of disjoint products (RSDP) with ordering heuristics are used for system reliability calculation; and the relative probability importance of each basic component and system reliability with and without forehead information are given at last. The results show that the network flow model works well on CRH3 bogie, and can support as guidance of bogie system design, daily system operation and predictive maintenance.

The Cyber-Physical System (CPS) of Railways is an intelligent integration system of information acquisition, data fusion, state identification, knowledge inference, system optimization and control. CPS presents a higher combination and coordination between physical and computational elements. It intends to enable the railway system itself to sense, analyze, determine, control, collaborate and behave autonomously, which has become a mainstream research direction of railway system in next generation. The 3rd International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT2017) in Changsha, Hunan Province, China, October 20–22, 2017 brought together worldwide practitioners, leading researchers and postgraduates showcasing state of the art research, innovation and industry practice at all levels and in all process in the electrical engineering, information technologies and sciences, software engineering and knowledge engineering for rail transportation. 4 research papers and 1 research note included in this special issue are selected from 138 accepted papers at EITRT2017 on the basis of a peer review process. The papers included in this special issue of IJSEKE focus on the application of knowledge engineering techniques to software development and railway system analysis, and demonstrate the state-of-the-art of Railway CPS.

This paper presents a risk-based maintenance decision making modeling methodology for railway asset maintenance optimization, which takes risk and maintenance cost objectives into consideration in the decision making process. A bottom-up risk analysis approach has been developed by using fuzzy reasoning approach (FRA) and fuzzy-analytical hierarchy process (Fuzzy-AHP) to produce a risk model. A total cost model has also been developed to estimate repair/renewal, maintenance and performance review costs. A risk-based maintenance decision making support model has then been developed by integrating the risk model with cost model in which multi-criteria decision making (MCDM) techniques are employed to process the proposed risk-based maintenance decision making support model. An illustrative example on a section of a track system maintenance decision selection is used to demonstrate the application of the proposed methodology. The results show that by using the proposed methodology the qualitative and quantitative risk data and information with maintenance costs associated with railway assets can be evaluated efficiently and effectively, which provide very useful information to railway engineers, managers, and decision makers.

The wireless monitoring system is always destroyed by the insufficient energy of the sensors in railway. Hence, how to optimize the communication protocol and extend the system lifetime is crucial to ensure the stability of system. However, the existing studies focused primarily on cluster‐based or multihop protocols individually, which are ineffective in coping with the complex communication scenarios in the railway wireless monitoring system (RWMS). This study proposes a hybrid protocol which combines the cluster‐based and multihop protocols (CMCP) to minimize and balance the energy consumption in different sections of the RWMS. In the first hierarchy, the total energy consumption is minimized by optimizing the cluster quantities in the cluster‐based protocol and the number of hops and the corresponding hop distances in the multihop protocol. In the second hierarchy, the energy consumption is balanced through rotating the cluster head (CH) in the subnetworks and further optimizing the hops and the corresponding hop distances in the backbone network. On this basis, the system lifetime is maximized with the minimum and balance energy consumption among the sensors. Furthermore, the hybrid particle swarm optimization and genetic algorithm (PSO‐GA) are adopted to optimize the energy consumption from the two‐layer hierarchy. Finally, the effectiveness of the proposed CMCP is verified in the simulation. The performances of the proposed CMCP in system lifetime, residual energy, and the corresponding variance are all superior to the LEACH protocol widely applied in the previous research. The effective protocol proposed in this study can facilitate the application of the wireless monitoring network in the railway system and enhance safety operation of the railway.

Snow detection and removal from video images is very challenging. Normally the snowflakes affect only on a very small region of an image, hence the confusion to determine which region should be considered and which one should not. In this paper, a frame difference method with five successive frames is first presented to detect the snow pixels from image background, but the method didn't work well in the case of heavy snow. Then a new technique has been implemented which uses the L0gradient minimization approach to remove the snow pixels. This technique can control how many non-zero gradients are resulted in the image, and is independent of local features, but instead locates important edges globally. These salient edges are preserved and the low amplitude and insignificant details are diminished. The snow pixels are then removed in this way. Experimental results show that this method is a highly efficient algorithm even under heavy snow conditions, while preserving the details of the image.

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.

Passenger flow safety detection in high-speed railway transport hub is considered in this paper.For accurately detecting the passenger flow safety, an improved watershed algorithm and a recognition algorithm are respectively proposed in sensor-based detection process.Computational experiments on sensor data from a specific Chinese high-speed railway transport hub show that the proposed algorithms are effective and efficient for detecting the passenger flow safety in high-speed railway transport hub.

Accurate real-time crash risk evaluation is essential for making prevention strategy in order to proactively improve traffic safety. Quite a number of models have been developed to evaluate traffic crash risk by using real-time surveillance data. In this paper, the basic idea of traffic safety region is introduced into highway crash risk evaluation. Sequential forward selection (SFS), principal components analysis (PCA) and least squares support vector machine (LSSVM) are used to estimate the traffic safety region and classify the traffic states (safe condition and unsafe condition). The proposed method works by first extracting state variables from the observed traffic variables. Two statistics [Formula: see text] and squared prediction error (SPE) are calculated by SFS–PCA and used as the final state variables for traffic state space. Next, LSSVM is used to estimate the boundary of traffic safety region and identify the traffic states in the traffic state space. To demonstrate the advantage of the proposed method, this study develops two crash risk evaluation models, namely SFS–LSSVM model and PCA–LSSVM model, based on crash data and non-crash data collected on freeway I-880N in Alameda. Validation results show that the method is of reasonably high accuracy for identifying traffic states.

In order to quantitatively and systematically explain the accident occur process and assess the risk for the complex system, this paper proposes a new accident-causing analysis model, i.e. perturbation-safety region (P-SR) model.In this model, the safety region definition is introduced for the quantitative description of the system safe status; also the change process of the system risk is analyzed.The four relative parts included in this model are described in details, such as the risk resource part, the perturbation part, the alarm and system change part, and the accident part.Finally, the proposed model is applied to railway transportation system, and the Wenzhou train collision is systematically analyzed, also the specified control measure for the train emergency dispatch is demonstrated.

Facing the important issues of safety analysis and assessment for the train service state, an online quantified safety assessment method based on the safety region estimation and hybrid intelligence technologies was proposed in this paper. First, the previous researches on the safety analysis and assessment were briefly reviewed for the train itself and its key equipment, and the existential problems were further pointed out. Then, using the safety monitoring data and the safety region estimation theory, a new online safety assessment method with data-driven was put forward, which was followed by a detailed description of the concrete implementation steps including the EMD (Local Mean Decomposition) and EM (Energy Moment) based safety risk evaluation index selection, Interval Type 2 Fuzzy C-Means (IT2FCM) clustering based safety region boundary calculation modeling and safety risk grading. Finally, in order to verify its performance through experiments, the above method was applied in analyzing and evaluating service states of the rolling bearings, the key equipment of the train, on the basis of mass field data. The experimental results indicate that this method is valid.

We analyze the application of regular increasing monotonic(RIM) quantifiers for parametric weighted ordered weighted average (WOWA) operator with piecewise linear function in decision problems. It can be used to represent various attitudes of the decision makers in forms of risk pessimistic, risk neutral and risk optimistic, respectively. Then, some properties of parametric WOWA operator with piecewise linear function are provided. Expected utility theory becomes a special case of parametric WOWA operator with risk neutral attitude. Different attitudes of the decision makers can be implemented by changing the values of parameter in parametric WOWA operators according to application context. Finally, the paper illustrates our theoretical results with an example of introducing and pricing new product.

In this paper, we give two direct centroid computation methods for fuzzy numbers: One is to use the membership function and the other is to use the alpha-cuts. Compared with the current centroid computation methods, the new methods are simple both in expression and computation. Weighted samples computation method are also proposed to improve the computational accuracy with numerical integration technique. Three examples illustrate the application of proposed methods.

Karnik-Mendel (KM) algorithms type reduction are commonly used in the centroid computation of interval type-2 fuzzy logic systems (IT2 FLSs). Some properties and improvements of the KM algorithms have be proposed. This paper proposed a new iteration formula for the centroid computation of interval type-2 fuzzy sets, which is faster than the current computation methods and can be used as a direct method for the centroid computation of interval type-2 fuzzy sets.

A fuzzy optimization model based tolerance approach is proposed to handle timetable rescheduling in high speed railway during speed restriction period. As the limited speed and headway time are not crisp figures in practice especially when some natural hazards happen or some equipment failure, tolerance approach is introduced with the fuzzy membership functions of the original objective and soft constraints to find an new optimal objective with little slack of constraints. The original objective is treated in the same manner as the soft constraints, so the model is symmetric. The proposed fuzzy rescheduling model is simulated on the busiest part of a high speed railway line in China. The entire case study shows the significance of fuzzy optimization in case of speed restriction. The results shed light on how we could choose a better limited speed and headway time, so that the number of seriously impacted trains can be reduce greatly with little cost and risk.

An architecture of sensor network for urban traffic information acquisition is proposed. The hybrid communication modes include CAN, ZigBee and Ethernet, which can satisfy the requirements of wired and wireless, real-time and massive data transmission. The various kinds of nodes and the prototype sensor network were developed and deployed in Beijing. The test results show the architecture, hybrid communication mode, various sensor nodes and the sensor network proposed in the paper are practical and feasible. This kind of sensor network can be used in traffic surveillance to resolve the problems of present information acquisition.