Railway Track Crack Research Articles

IoT Based Robotic Car for Railway Track Crack Detection System

IoT Based Robotic Car for Railway Track Crack Detection System

Railway Track Crack Detection

Railway Track Crack Detection

Accident Avoidance using Railway Track Detection System using IoT

An IoT-based railway track crack and object discovery framework is an innovative arrangement for guaranteeing the security of railroad frameworks. The framework employs a combination of sensors and cameras that are introduced along the railroad track to screen it for any signs of harm or hindrances. The sensors can identify vibrations and changes within the track, which can show the nearness of a break or other sort of harm. The cameras can capture pictures and video of the track, such as fallen branches, flotsam, and jetsam, or indeed individuals or creatures. Once the framework recognizes an issue, it can send an alarm to the railroad administrators or upkeep workforce, permitting them to rapidly address the issue sometime soon if it causes any mischances or delays. An IOT-based railroad track break and protest location framework can incredibly move forward the security and proficiency of railroad operations. Key Words: Embedded system, Internet of things, ultrasonic sensor, IR detector, Arduino Uno, GPS

IoT Based Railway Track Crack Detection System

IoT Based Railway Track Crack Detection System

Development of Railway Track Crack Detection System using Arduino

The Indian Railways has one of the largest railway networks in the world, criss- crossing over 1,15,000 km in distance, all over India. However, with regard to reliability and passenger safety Indian Railways is not up to global standards. Among other factors, cracks developed on the rails due to absence of timely detection and the associated maintenance pose serious questions on the security of operation of rail transport. A recent study revealed that over 25% of the track length is in need of replacement due to the development of cracks on it. Manual detection of tracks is cumbersome and not fully effective owing to much time consumption and requirement of skilled technicians. This paper is aimed towards addressing the issue by developing an automatic railway track crack detection system with the proliferation of Internet of Things (IoT). It is possible to collect massive amount of data for localization and tracking of Railway Fault Detection.

RAILWAY TRACK MONITORING SYSTEM

In India rail transport occupies a prominent position in providing the necessary transport infrastructure to sustain. needs of a rapidly growing economy. Today, India possesses the fourth largest railway network in the world. However, in terms of the reliability and safety parameters, we have not yet reached truly global standards. The main problem about a railway analysis is detection of cracks in the structure. If these deficiencies are not controlled at early stages, they might lead to a number of derailments resulting in a heavy loss of life and property. This system proposes a cost-effective solution to the problem of railway track crack detection utilizing IR (Slot sensor) assembly tracks the exact location of faulty track which then mended immediately so that many lives will be saved Key Words: Slot sensor, IR Transmitter and IR Receiver, light, lasers,

Real time railway track crack analysis using multi-level classification

Rail transportation system remains one of the most cost effective and suited means of passenger and goods transportation for both long distance and suburban travel. Hence it is a critical task to ensure regular railway maintenance. This work explores the scope of detecting cracks and also pedestrians in the track by making use of deep learning and image recognition system. This task becomes very challenging as the cracks in the track are varying and the pedestrian can take wide range of poses. This system make use of a conventional Convolutional Neural Network (CNN) along with Support Vector Machine for recognition of crack defective from the dataset. SVM is the binary classifier used, while CNN was used as an automated feature extractor. The dataset was used to train and test the algorithm that was implemented in the proposed model. CNN’s receptive field aids in extracting the most recognizable aspects from these images automatically. The experimental findings show that the suggested framework is successful, with a recognition accuracy of 78% throughout the dataset and a template based lane detection method is used. Lane tracking is done with edge detection and the position of a region of interest in vanishing point in tack image is used for identifying the land departure. This detector on real time video with the region of interest selection and achieve in 23 fps.

Solar Powered Fault Detection System for Railway Tracks

Indian Railways has one of the world's largest networks. Train movement is always reliant on railway rails alone. If one of these rails develops a crack, it becomes a huge issue. Many railway accidents occur as a result of the presence of a crack. The most difficult aspect of a railway analysis is detecting structural faults. If these flaws aren't addressed early on, they could lead to a series of derailments, resulting in a significant loss of life and property. The proposed railway track error detection system detects faulty railway tracks automatically and without the need for human intervention. This project intends to create a solar based autonomous railway track crack detection vehicle that uses a microcontroller and ultrasonic sensors to detect cracks along its journey. The ultrasonic sensor detects the fracture and objects, sending the information to the microcontroller, which quickly stops the train. Solar panels are utilized in this vehicle to absorb solar energy [1], which is then transformed into electrical energy, which is then used to charge a lead acid battery, which then provides the necessary power to a DC motor. This energy is subsequently sent to the DC motor, which drives the wheels. Sensors are utilized to detect cracks, and if a crack is detected, the system will halt and sound an alarm.

RAILWAY TRACK – CRACK DETECTION SYSTEM BY USING ARDUINO MICROCONTROLLER

- In India railways transportation service is the cheapest and most convenient mode of passenger transport as well as for long distance and suburban traffic. Almost 80% of the transport in India is being carried out by railway network. The main cause of the accidents happened in railways are railway track crossing and unidentified crack in rail tracks. About 60% accidents are happened at railway track crossing and due to crack in railway tracks resulting in loss of precious life of passengers and loss of economy. Therefore, there is a need to have new technology which will be robust, efficient and stable for both crack detection in railway track as well as object detection. This project discusses a Railway track crack detection using sensors and is a dynamic approach which combines the use of GPS tracking system and GSM module to send alert messages and the geographical coordinate of location. An Arduino Microcontroller is used to control and coordinate the activities of this device.

Cost Effective Railway Track Fault Detection Using Image Processing

The efficiency of the tracks plays an important role in the functioning of the railway system. We propose an effective solution to detect the railway track crack and decrease the number of accidents. A crack detection algorithm is proposed which will improvise the existing system of manually checking the track health. Our model is designed to capture images of the tracks and check for the minor cracks. It will also estimate the crack length and give report of railway track health.

Arduino Based Programmed Railway Track Crack Monitoring Vehicle

Indian Railroad is one of the biggest railroad systems on earth. Despite the fact that there is an enormous development in the Indian railroads, but some of the accidents occurs due to cracks which occur in the railway track. In this paper we are thinking about the serious issues that result in accidents. To overcome this major problem we have proposed another monitoring train that uses an ultrasonic sensor which is used to detect the crack in the railway track and used to send SMS and call via GSM and GPRS module with the help of Arduino Uno. During different seasons the track is expanded or contrasted due to which splits may happen. This testing train is used to detect the crack, when detects, the train stops and sends the signals to the nearest station. This intelligent system provides protection and online monitoring system.

Railway Track Crack Detection Technique

Railway Track Crack Detection Technique

Implementation of Railway Track Crack Detection and Protection

In this world people uses various types of transportation system to travel from one place to another place. Mostly they give importance to public transportation for safer journey. At the same time the transport departments check out the safety measures implemented in them. The proposed system is suitable for railways transportation to identify the cracks in the railway tracks earlier and prevent the accidents. In this paper to use crack detection sensor, this will be placed in the train engine. By this, if some crack is detected on the track the train starts to slow and stop at respective point automatically and exact place of crack would be given to control room. Secondly the next cause of accidents is prevented from two trains opposite in same track by using the same sensors fitted in the engine, if the sensor senses the same signal from opposite train then it automatically applies the brake and stops the train at certain distance. The derailment causes several loses in railway accidents. The proposed system introduces Bluetooth based technology, to prevent the trains accident. The Bluetooth device is installed at each front end of the locomotive. If the train starts to derail, automatically signal is breaked and an alert is given to engine driver and on the other emergency brake is applied automatically. The main aim of the work is to avoid the train accidents without manual power.

Railway Track Crack Detection Vehicle

Railway Track Crack Detection Vehicle

Rotating Electromagnetic System for Railway Track Crack Detection

The aim of this paper is to provide an alternative methodology, high reliability, in order to monitor, exploiting non-invasive techniques, railway track. In this particular case study, the presence of structural defects is assessed through an innovative system. The proposed approach provides the use of rotating magnetic field. Within this framework, the Eddy Current Techniques (ECTs) have greatly increased their importance for their capacity to detect the magnetic field variations caused by the presence of material alterations. Whilst the researches in this field propose various solutions to the detection of flaws (in surface and subsurface), a still open problem concerns the difficult detection of defects due to the geometrical complexity of the structure and the particular orientations of the crack. Particularly, this contribution proposes an approach based on Finite Element (Finite Element Method, FEM) for the modeling of railway tracks, in order to obtain a rapid and precise assessment about their integrity.

Automatic Track Inspection in Railway Network

In India rail transport occupies a prominent position in providing the necessary transport infrastructure to sustain needs of a rapidly growing economy. Today, India possesses the fourth largest railway network in the world. However, in terms of the reliability and safety parameters, we have not yet reached truly global standards. The main problem about a railway analysis is detection of cracks in the structure. If these deficiencies are not controlled at early stages they might lead to a number of derailments resulting in a heavy loss of life and property. This project proposes a cost effective solution to the problem of railway track crack detection utilizing RF control assembly which tracks the exact location of faulty track which then mended immediately so that many lives will be saved.