International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
AUTOMATIC RAILWAY TRACK CRACK DETECTION SYSTEM USING IOT R. Dharanidaran1, S. Jagan2, B. Anandhakeerthi3, S. Kaveyen4, U. Gowtham5 2Assistant Professor, Department of Mechatronics, Sri Manakula Vinayagar Engineering College, Puducherry, India 1,3,4,5U.G.Student, Department of Mechatronics, Sri Manakula Vinayagar Engineering College, Puducherry, India
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Abstract - The objective of this initiative is to tackle the
widespread problem of railway accidents in India, which frequently lead to fatalities as a result of faults in railway tracks, especially fractures. The suggested remedy is using GPS systems in conjunction with sensor technology to pinpoint the exact position of railway track cracks. The Node MCU IoT module is used by the system to gather data from the sensors and send it to the cloud for processing and archiving. At89s52 microcontroller is utilized to supervise the system's overall operation, guaranteeing the smooth integration and synchronization of diverse components. Through real-time crack detection and cloud-based transmission of this information, railway authorities can quickly implement preventive actions like temporary closures or track maintenance to reduce the likelihood of accidents.
Furthermore, our model emphasizes the importance of comprehensive training programs for railway personnel to ensure proper implementation and maintenance of the proposed safety measures. Fig-1 depicts the crack in the railway track. By fostering a culture of safety and accountability, we aim to create a transportation system that prioritizes the well-being of passengers and minimizes the occurrence of tragic accidents on Indian railways.
Key Words: At89s52 microcontroller, IR Sensor, Node MCU, IOT Cloud, GPS Module
1.INTRODUCTION Certainly, the railway system in India, while being one of the most affordable and efficient modes of transportation, does face significant challenges, particularly regarding safety. The occurrence of accidents resulting in loss of lives and injuries underscores the need for modernization and enhanced safety measures within the railway infrastructure. To address these issues, our model proposes the development of a modernized and secure transportation system. This system would incorporate advanced technologies and methodologies to ensure the safety of passengers and prevent accidents. One crucial aspect is the implementation of predictive maintenance systems that can detect potential issues such as track cracks caused by natural changes in physical and chemical properties. By utilizing sensors and monitoring systems, our model aims to continuously assess the condition of railway tracks and identify early signs of wear, damage, or potential hazards. This proactive approach allows for timely maintenance and repair, significantly reducing the risk of accidents caused by track defects. Moreover, our model advocates for the integration of cutting-edge safety features such as automated signaling systems, collision avoidance technology, and real-time monitoring of train operations. These advancements not only enhance safety but also improve the overall efficiency and reliability of the railway network.
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Fig -1: Crack in Railway Track In our proposed system, we implement an infrared (IR) sensor as the primary component for crack detection along railway tracks. The IR sensor is strategically placed to monitor the track’s surface continuously. When a crack is detected, the IR sensor triggers the system to initiate further actions. Upon crack detection, the system activates a GPS module integrated into the setup. The GPS module accurately determines the geographical coordinates of the location where the crack is detected. These coordinates provide precise information about the exact spot on the railway track where maintenance or repair is required. Next, the system employs an IoT framework, utilizing Node MCU as the intermediary device. Node MCU collects the crack detection data and the corresponding GPS coordinates. It then establishes a connection to the cloud infrastructure for data transmission. The cloud platform receives the crack detection data along with the GPS coordinates from Node MCU. This information is processed and stored securely in the cloud database. Additionally, appropriate alerts or notifications can be generated and sent to concerned authorities or railway personnel in real-time. By leveraging this integrated approach, railway authorities can swiftly respond to crack detections, allowing for timely maintenance or repair actions to be taken. This proactive measure significantly reduces the risk of accidents and
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