Discovering of Cracks in Railway Track by using IoT

Discovering of Cracks in Railway Track by using IoT

Shruthi B Gowda

Dept. of Computer science and Engineering Vivekananda institute of technology Bangalore

Shwetha

Dept. of Computer science and Engineering Vivekananda institute of technology Bangalore

Sinchana M P

Dept. of Computer science and Engineering Vivekananda institute of technology Bangalore

Santhrupthi D

Dept. of Computer science and Engineering Vivekananda institute of technology Bangalore

Ankitha M K

Dept. of Computer science and Engineering Vivekananda institute of technology Bangalore

Abstract In India railway is one of the most common means of transport, which is the fourth largest railway community in the world. Even though Indian railways has an outstanding boom, it remains plagued because of some of the major issues like problem in gate crossing, fire accidents and problem in the track which remains unmonitered causing derailment. The tracks contract and expand due to changes in season. Due to this crack may develop on the track. This proposed system identifies the cracks and the sensors and inform the control room through an SMS using GSM and GPS module.

Keywords Components; railway track crack detection, safety management, railway supervision (key words)

1. INTRODUCTION

   Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. This helps system to detect only crack and helps to remove unwanted objects. Transport is very important to carry the passengers and goods from one place to another. Economic level is mainly depending on increasing the capacity and level of transport. In this paper we are going to use IR sensor to detect the crack in rail road, when the crack is detected its latitude and longitude values are send as a message to nearby station by using GPS and GSM service. Then Ultrasonic is used for the surveying process. Then other important component is This system used during railway track inspection. The proposed system is able to detect deeper cracks with 80% success rate and minor cracks with 50-60% accuracy. In present era, roadways and railways are important part of human beings because without these transportationsit is impossible to travel for a long distance. In India, most of the people prefer trains for travelling rather than carbusses 0r any other roadways because the road contains large number of holes due to which jerking Trek place.In India, most of the people prefer trains for travelling rather than car busses or any other roadways because the road contains large number of hole due to which jerking take place. If there is a senior citizen it would be very difficult for them to travel form these types of

   roads thats why people give more priority to railways for travelingrather than roadways.

2. LITERATURE SURVEY

   India procures a major position in the list of quickly developing countries with notable and appreciable advancements in the field of railway network. Derailments contribute to several railway accidents that have been caused. This is mainly due to the cracks in the railway track. These cracks usually go unobserved due to irregular maintenance. Manual track monitoring also adds to this issue of railway accidents. In this work, an autonomous testing train has been proposed for examining and spotting the cracks that are formed in the railway track. It is capable enough to detect even the small cracks caused on the railway tracks. The proposed testing train moves along the alienated path, which is the railway track and detects the cracks and limits in the track the relevant officials are intimated with the exact location of the crack [1].

   For better inspections and security, we need an efficient railway track crack detection system. In this research, we present a computer vision- based technique to detect the railway track cracks automatically. This system uses images captured by a rolling camera attached just below a self-moving vehicle in the railway department. The source images considered are the cracked and crack-free images. The first step is pre-processing scheme and then apply Gabor transform. In this paper, first order statistical features are extracted from the Gabor magnitude image. These extracted features are given as input to the deep learning neural network for differentiate the cracked track image from the non-cracked track image [2].

   In this paper, we present an automated system based on microcontroller and sensors to overcome the problem of faults in tracks and to identify the moving object or animal on the tracks. The system designed is an autonomous robot consist of PIR and Ultrasonic sensors, coupled with GPS and GSM for providing the real time alert. Global Mobile Communications

   System (GSM), GPS (Global Positioning) System) and broken rail track detection, based on the microcontroller, are an effective method of detecting cracks present on tracks, thus preventing train derailment. This device uses two stations to detect cracks on the path through TSOP sensors that transmit sinus waves to the ideal path. The crack detection on the railway line is used to locate the crack on tracks. The device proposed is used before 10 km to detect the railway crack [3].

   The proposed system utilizes CNN to detect the faults in the railway tracks with the help of images. Their characteristics are obtained to extract the default railway track. This model helps to reduce the manual inspection work. The CNN algorithm, the ground truth databases on images of masks are utilized. The algorithm improves the accuracy of railway crack detection effectively under convoluted conditions. The convolutional neural network (CNN) is mainly designed for recognizing. CNN is performed outstandingly including both MNIST and SVHN datasets. Broken Rail Detection System using RF Technology is proposed. Mainly this system is used to detect the broken rail and provide the information exactly about the tracks. This model presents a synthesis of the art of computer vision this mainly explains the categories of the methods in computer vision methods, they are used to automate the damage and process detection [4].

   The presented methodology is to monitor endlessly cracks in tracks, obstacles on rail tracks and any other train running on the same track oppositely by using sensors. When identified, the device will send an alert to the driver to prevent the accidents. The flame sensor detects it and sends a signal to the microcontroller and driver when the train compartment catches fire. With the help of this paper, we try to overcome few of the issues in railways as well as modernize the compartment. The crack detection track system has been designed to minimize rail traffic crack crashes. The paper describes the entire hardware framework and programmer architecture. The system accuracy and reliability are good for the identification of rail surface crack by experimentation [5].

   System has many techniques, such as Ultrasonic Techniques, Electromagnetic Techniques and GSM Techniques. For the propose of this research, we study characteristics of railway track using acoustic emission, the device which is widely used for application such as crack Nowadays, railway track crack detection detection in aerospace space grade steel, detection of defect s in rolling element bearing, fatigue crack growth detection. And use the MFCC (Mel Frequency Cepstral Coefficients) method which is generally used or low frequency to extract the feature of each railway track. According to that, we chose to adjust the MFCC method for an acoustic feature extraction in order for condition to be suitable used for this system, because the railway track crack detection system, we are interested in concentrates at 100-400 kHz [6].

   Indian Railways is considered to be a microcosm of India and has an enormous importance in Indian economy. Accidents due to defects in tracks account for 40%-50% of the total accidents, which is a significant figure. This data is published in RDSO (Railways Design & standards organization, India).

   Aim of our Discovering of cracks in railway track by using IoT Dept. of CSE, VKIT 2021-22 Page 11 research is to detect and record track level and gauge irregularities, by observing Hunting Oscillations causing the major train accidents that results into massive loss of lives and property. Hunting Oscillations are nothing but the unnecessary movement of a train coach in horizontal and vertical direction. This includes embedded C programming for ATMEGA32, GPS interface, SD card interface along with 16×4 LCD, power supply section using necessary regulators and a cloud- based web application for remote monitoring [7].

   Rails are the carrier of motion in rails transit systems. Keeping the surface smooth and defect detection are essential for the safe operation of rail transit. Traditional method includes visual method, eddy current detection method, ultrasonic detection method, machine vision detection etc. The whole crack detection system is designed as a vehicle-mounted structure and runs on the rail is collected by a high precision linear coupled device (CCD) industrial camera. The crack coefficient of variation, and the similarly between pixels is calculated to further distinguish the crack region and the shadow region. The extracted crack image highlights the crack target. According to the line- loading characteristics of the ratio of the length and length of the binaries connected region are used to discriminate. The classical pixel integral projection is used to perform the integral projection of the horizontal and vertical directions of the crack [8].

3. PROPOSED SYSTEM

   To overcome the restrictions of existing method that are employed for detecting faulty tracks, the proposed system can be utilized. An Arduino UNO board is utilized in this proposed system. This is an open-source electronic prototyping plan of action which simplifies the coding then allows the users to create interactive electronic devices. The Arduino UNO detects the sensor output and transfuse the signal as an SMS to the base station whenever a crack is detected.

4. METHODOLOGY

   The unit is a robotic unit with four D.C.-powered wheels. Engines by motor driver assistance. The engine manager L293D is in use. In relation to the current Indian rail track crack detection system, which requires 5 to 6 people during the monitoring period, an individual can effectively handle the proposed system during the monitoring period. Microcontroller used by Arduino Uno. The robot starts on the railways to start the crack detection process when power supplies are allowed. The UNO sensor module is used to detect a break in line. If the robot detects a crack while guiding the route, it stops immediately and warnings are obtained with the GPS module to the buzzer and to locate the crack coordinates exactly. The system also has an ultrasonic sensor that detects the object across the tracks. If any animal or moving object gets detected across the tracks the train will reduce the speed and eventually come to halt. The LCD displays the crack location GPS co-ordinates.

   An SMS consisting of a crack detected shall be sent to the railway authorities' registered control number whenever a

   crack is detected. Besides that, it also sends in the SMS the crack position displayed on the LCD.

5. CONCLUSION

India railway is the most common means of transport Even Though Indian railways has an outstanding boom, it remains plagued because of some of the major issues like problem in gate crossing, fire accidents and problem in the track which remains unmonitered causing derailment. The tracks contract and expand due to changes in season. Due to this crack may develop on the track. The proposed system has the ability to detect the cracks and techniques which include low cost, low power consumption, fast detecting system without human intervention and less analysis time.

REFERENCE

[1] Raghunandan Srinath, Jayavrinda Vrindavanam Differential Sensor for Reliable Localization of Potential Cracks in Railway Tracks,2020

[2] Satya Srikanth Palle, Jijesh JJ. Modernized compartment with safety measures in railway,2020

[3] Narate Vongserewattana, Wara Suwansin, Pattarapong Haruki, Punnavich Phatsornsiri Validation of Acoustic Emission Railway Track Crack Analysis Using MFCC,2019

[4] Shubhankar Khaladkar, Srishti Gawande. Internet of Trains

Oscillometer,2019

[5] FU Shengwen, Jiang Zhanjun. Research on Image-based Detection and Recognition Technologies for Cracks on Rail Surface,2019

[6] Sanket Salvi, Shashank Shetty. AI based Solar Power Railway Track Crack Detection and Notification System with Chatbot Support,2019

[7] Suhas BN, Sushen Bhagavat, V Vimalanand, Suresh P. Wireless Sensor Network based Monitoring of Railway Tracks,2018

[8] Alexander M. Boronakhin, Daniil Yu. Larionov, Liudmila N. Podgornaya, Anna N. Detection and Classification of Rail Track Flaws Using Inertial and Magneto metric Sensors,2018