Automatic Visual Inspection and Condition-Based Maintenance for Catenary

Defects on catenary components are a major part of device faults as a result of a much higher tension on high-speed catenary, such as looseness of bolts, component broken, and component missing. Traditional inspection on catenary components has to be performed only at night with human eyes. Not only the inspection speed is very slow but also the inspection results are not reliable, as a result of the poor lighting environment and long-time working tiredness. In this chapter, we present an automatic visual inspection system for checking the status of components on catenary. A dedicated designed camera system is mounted on an inspection car, which covers almost all the components to be checked and gives great details of each component. Considering the great data storm at each catenary post, high-performance servers with GPU acceleration are used, and technologies of multi-thread and parallel computing are exploited. Furthermore, an intelligent analysis framework is proposed, which uses structural analysis to localize each component in the image and perform automatic detection based on different features such as geometry, texture, and logic rules. The system has been successfully used in China’s high-speed railways, which shows great advantages in the catenary inspection application

Contactless rail track condition analysis approach using image matching

Demiryolu ulaşımı geçmişten günümüze kadar yaygın olarak kullanılan en önemli ulaşım türlerinden biridir. Demiryolu sistemleri yük ve yolcu taşımacılığında yaygın olarak kullanılmaktadır. Demiryolu hattında birçok arıza oluşabilmektedir. Demiryolu araçlarında veya hatlarında oluşabilecek arızalar ulaşımı olumsuz etkilemektedir. Bu arızaların erken teşhis edilmesi için durum izleme oldukça önemlidir. Genellikle ray, travers ve bağlantı plakalarından kaynaklanan arızalar ortaya çıkmaktadır. Bu çalışmada, demiryolu hattını oluşturan bileşenlerin izlenmesi için görüntü işleme tabanlı bir yöntem önerilmiştir. Sağ ve sol rayların izlenmesi için iki tane kamera kullanılarak bir deneysel yapı oluşturulmuştur. Demiryolu hattı üzerine kurulan deneysel yapı ile farklı durumlarda videolar alınmıştır. Alınan videolar üzerinde YCbCr renk uzayı, Canny kenar çıkarımı ve köşe tespit algoritması kullanılarak demiryolu bileşenleri tespit edilmektedir. Bu çalışmada ray, travers ve bağlantı plakasının birleştiği kısımlar tespit edilmektedir. Oluşturulan deneysel yapı ile farklı tür demiryolu hatlarında da görüntüler alınarak sonuçlar test edilmiştir.Rail transport is one of the most important modes of transport commonly used in the past to the present. Rail systems are widely used in passenger and freight transport. Many failures can occur on railways. The failures occured on railway tracks or vehicles may negatively affects the transportation. Condition monitoring is very important for the early detection of this failure. The failures especially due to rails, sleepers and tie plates. In this study, an image processing-based method has been proposed for monitoring the components of the railway. An experimental structure using two cameras for monitoring of right and left rail is formed. Samples videos in different situations were taken with the experimental structure founded on the railway track. The railway components were detected on sample videos by using YCbCr color space, Canny edge detection and corner detection algorithms. In this study, the rail, the tie plate and the joins of the traverse are determined. The experimental structure is used on different railways and the result are tested.Bu çalışma TÜBİTAK (Türkiye Bilimsel ve Teknolojik Araştırma Kurumu) tarafından desteklenmiştir. Proje No: 114E202

A Smart UAV Platform for Railroad Inspection

Using quadcopters for analysis of an environment has been an intriguing subject of study recently. The purpose of this work is to develop a fully autonomous UAV platform for Railroad inspection The dynamics of the quadrotor is derived using Euler\u27s and Newton\u27s laws and then linearized around the hover position. A PID controller is designed to control the states of the quadrotor in a manner to effectively follow a vision-based path, using the down facing camera on a Parrot Mambo quadrotor. Using computer vision the distance from the position of the quadrotor to the position of the center of the path was found. Using the yaw controller to minimize this distance was found to be an adequate method of vision-based path following, by keeping the area of interest in the field of view of the camera. The downfacing camera is also simultaneously observing the path to detect defects using machine learning. This technique was able to detect simulated defects on the path with around 90% accuracy

Development of UAV-Based Rail Track Geometry Irregularity Monitoring and Measuring Platform Empowered by Artificial Intelligence

Rail tracks need to be consistently monitored and inspected for problems associated with rust, deformation, and cracks that, at their worst, can cause catastrophic train derailments. Many non-destructive testing approaches have been explored and extensively utilized to help inspect rails’ health, but most of them require intensive human power and/or heavy sensor systems (e.g. total stations, manual/car-mounted trolly, etc.) that are not efficient or convenient to cover a long range of rails and may interfere with the normal operation of trains.In light of the rapid development of unmanned aerial systems/vehicles (UAS’s/UAVs) and high definition photographic and optical distance measuring sensors, this paper proposes a novel UAV-based rail track irregularity monitoring and measuring platform that can remotely inspect the geometry irregularity of tracks at various angles and cover a long distance by only a few personnel. By mounting a light distance and range (LiDAR) scanning sensor and a data acquisition system on the UAV, we can continuously collect 3D point cloud data (PCD) frames that reflect the surfaces of tracks, ground, and other objects. Data points in these PCD frames are manually annotated into two classes: rail tracks and background. Then, annotated PCD frames are pre-processed and fed to train a state-of-the-art machine-learning-based 3D point cloud semantic segmentation network, RandLA-Net, to assign each point into one of the two aforementioned classes, so that point clusters that represent rail tracks can be extracted. The trained model can be deployed for real-time distinction between rails and background. Then, principal component analysis (PCA) and multiple regressions are conducted to identify the top and inner surface of the rails. In the end, various geometry measurement of rails, such as gauge, cross level, etc. can be performed to inspect any irregularities. The geometry measurement obtained by the proposed UAV-LiDAR-based framework is compared against standard official value of each geometry. The evaluation results have confirmed the similar or the more advanced performance of the proposed platform with more terrain flexibilities

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work