Railway Infrastructure Monitoring Research Articles

Diagnosis of high‐speed railway ballastless track arching based on unsupervised learning framework

AbstractVehicle‐mounted detection methods have been widely applied in the maintenance of high‐speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging to label detection data, hindering the application of supervised learning approaches. To address these problems, this paper utilizes the longitudinal level irregularity data obtained from vehicle‐mounted detection, employing the concept of unsupervised learning for dimensionality reduction, combined with clustering algorithms and minimal label fine‐tuning, to design two frameworks: the fully unsupervised framework (FUF) and the few‐shot fine‐tuned framework (FFF). Experiments on dynamic detection data from a Chinese HSR line were conducted, comparing the performance of data dimensionality reduction, clustering, and classification under different strategy combinations. The results show that the improved variational autoencoder significantly enhances the performance of the encoder in dimensionality reduction, facilitating better feature extraction; the FUF achieves effective clustering outcomes without any labeled samples and its adjusted rand index score exceeded 0.8, showcasing its robustness and applicability in scenarios with no prior annotations; the FFF requires only a small number of labeled samples (labeling ratio of 5%) and achieves excellent performance, with metrics such as accuracy exceeding 0.85, thus greatly reducing the reliance on labeled data. This study offers a novel method for solving engineering issues with limited labeled data, providing an efficient solution for identifying track arching defects and advancing railway infrastructure monitoring.

Deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. Deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

Оценка возможности автоматического определения состояния искрового промежутка

Purpose: To consider the possibility of automatic determination of the spark gap condition. Methods: The article analyzes devices and methods of automatic control of the spark gap condition. It also presents their features and disadvantages, based on which conclusions are made concerning the convenience of operation in production, the quality of the spark gap condition monitoring, the possibility of using the system in automatic control systems. Results: A new modernization of spark gaps is proposed, which will allow not only to organize automatic monitoring of their condition, but also to introduce automatic monitoring of spark gaps condition into the system of railway infrastructure monitoring using unmanned aerial vehicles, which is currently the subject of special attention from the holding company JSC “Russian Railways”. The justifications for choosing this particaular new modernization include minimal capital investments, while maintaining the necessary and sufficient efficiency of spark gap monitoring. Also, there is the possibility of integration into the existing monitoring system with the use of unmanned aerial vehicles. Practical significance: The possibility of automatic determination of the spark gap condition is shown, which corresponds to the priority direction of digitalization of railway transport and railway infrastructure of the holding company JSC “Russian Railways”, specified in the holding company’s development strategies up to 2030 and in the perspective up to 2035.

Seeking a Sufficient Data Volume for Railway Infrastructure Component Detection with Computer Vision Models.

Railway infrastructure monitoring is crucial for transportation reliability and travelers' safety. However, it requires plenty of human resources that generate high costs and is limited to the efficiency of the human eye. Integrating machine learning into the railway monitoring process can overcome these problems. Since advanced algorithms perform equally to humans in many tasks, they can provide a faster, cost-effective, and reproducible evaluation of the infrastructure. The main issue with this approach is that training machine learning models involves acquiring a large amount of labeled data, which is unavailable for rail infrastructure. We trained YOLOv5 and MobileNet architectures to meet this challenge in low-data-volume scenarios. We established that 120 observations are enough to train an accurate model for the object-detection task for railway infrastructure. Moreover, we proposed a novel method for extracting background images from railway images. To test our method, we compared the performance of YOLOv5 and MobileNet on small datasets with and without background extraction. The results of the experiments show that background extraction reduces the sufficient data volume to 90 observations.

A collection of machine learning assisted distributed fiber optic sensors for infrastructure monitoring

Abstract In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.

A Review on Deep Learning Techniques for Railway Infrastructure Monitoring

A Review on Deep Learning Techniques for Railway Infrastructure Monitoring

Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods

Health monitoring of ballast in railway infrastructure is crucial to assure long-term structural stability. An efficient and sustainable management of maintenance operations is therefore fundamental for asset owners in setting up strategic and effective action plans. Amongst the available methods to assess the conditions of railway infrastructure, non-destructive technologies (NDT) are gaining popularity due to their capability to overcome main drawbacks from conventional routine methods, such as digging trenches and visually inspecting sections along the track. The present study reports an overview on the use of the ground penetrating radar (GPR) and the interferometric synthetic aperture (InSAR) technologies for a sustainable monitoring of railway infrastructure. Main conventional and non-destructive methods utilised for maintenance of railway ballast materials are presented, with a special focus on their sustainability. A review about research methods on the use of GPR and InSAR technologies for railway infrastructure monitoring is also reported, including main investigations carried out in the laboratory and the real-life environments. Furthermore, a conceptual framework based on an integrated approach between satellite-based and ground-based investigations is proposed, where network- and local-level information can be merged for a more effective detection of critical sections and the implementation of an advanced predictive maintenance system.

Railway Panorama: A Fast Inspection Method for High-Speed Railway Infrastructure Monitoring

At present, the comprehensive inspection trains of all countries in the world are equipped with an environmental video inspection system. The running environment is monitored by manual analysis of the video data, which is labor intensive and inefficient. In this paper, we propose a fast and intelligent inspection method for high-speed railway infrastructure monitoring. A railway environment video is acquired by an imaging device installed on a high-speed integrated detection train, and the forward-looking video is used to establish a panoramic imaging model by constructing the rectangular sampling area having a dynamic width from each frame of the video sequence, which covers the four side scenes of the railway and generate four panoramic images through image stitching technology. The experimental analysis indicates that proposed method makes the examination and analysis of video faster, simpler and more intelligent, which not only simplifies video content and reduces the time cost for users to browse video; but is also more feasible to run automatic identification algorithm on the compressed panoramic image for realizing the automatic intelligent detection of railway environment anomalies.

Monitoring of railway vehicles and infrastructure by portable dynamic scale

The article presents the devices for monitoring loads in railway transport, developed and manufactured by the Adaptronica company. Dynamic scales utilize portable transducers fixed in a non-invasive way to the rail foot. Load identification is based on the measurements of rail deformations caused by a passing train. The article presents the results obtained during the monitoring of railway lines, characterized by significant intensity of train traffic. Histograms of axial load values of the tested vehicles were determined. The measurements’ results were verified with the results obtained with a reference static scale or with the results obtained during passage of locomotives with a given mass. Keywords: portable dynamic rail scale, weight in motion, railway infrastructure monitoring

INTELLIGENT MACHINE VISION BASED RAILWAY INFRASTRUCTURE INSPECTION AND MONITORING USING UAV

Traditionally, railway inspection and monitoring are considered a crucial aspect of the system and are done by human inspectors. Rapid progress of the machine vision-based systems enables automated and autonomous rail track detection and railway infrastructure monitoring and inspection with flexibility and ease of use. In recent years, several prototypes of vision based inspection system have been proposed, where most have various vision sensors mounted on locomotives or wagons. This paper explores the usage of the UAVs (drones) in railways and computer vision based monitoring of railway infrastructure. Employing drones for such monitoring systems enables more robust and reliable visual inspection while providing a cost effective and accurate means for monitoring of the tracks. By means of a camera placed on a drone the images of the rail tracks and the railway infrastructure are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The preliminary obtained results are promising.

Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

Improving the resilience of infrastructures is key to reduce their risk vulnerability and mitigate impact from hazards at different levels (e.g., from increasing extreme events, driven by climate change); or from human-made events such as: accidents, vandalism or terrorist actions. One of the most relevant aspects of resilience is preparation. This is directly related to: (i) the risk prediction capability; (ii) the infrastructure monitoring; and (iii) the systems contributing to anticipate, prevent and prepare the infrastructure for potential damage. This work focuses on those methods and technologies that contribute to more efficient and automated infrastructure monitoring. Therefore, a review that summarizes the state of the art of LiDAR (Light Detection And Ranging)-based data processing is presented, giving a special emphasis to road and railway infrastructure. The most relevant applications related to monitoring and inventory transport infrastructures are discussed. Furthermore, different commercial LiDAR-based terrestrial systems are described and compared to offer a broad scope of the available sensors and tools to remote monitoring infrastructures based on terrestrial systems.

A Big Data Analysis Approach for Rail Failure Risk Assessment.

Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.