![SYSTEM REQUIREMENTS
Hardware requirements:
Arduino Uno
Liquid Crystal Display
IR Sensor
Motor Driver [L293D]
DC Motor
Nodemcu [ESP8266]
Fire Sensor
Ultrasonic Sensor
Relay
Slide 6](https://image.slidesharecdn.com/rtppt-240118113427-193858ef/85/railway-risk-assessment-in-IOT-based-systems-9-320.jpg)
![REFERENCES
[1]H. Gao, J. Shi, C. Bao, P. Li and G. Chen, "Fault prediction of track circuit compensation capacitor based on MFO-LSTM," 2023 CAA
Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China, 2023, pp. 1-5, doi:
10.1109/SAFEPROCESS58597.2023.10295732.
[2] M. Xiao and B. Liu, "Fault Diagnosis of Jointless Track Circuit Based on Adaptive Noise Completely Integrated Empirical Modal
Decomposition and Deep Belief Network," 2023 4th International Conference on Intelligent Computing and Human-Computer Interaction
(ICHCI), Guangzhou, China, 2023, pp. 64-68, doi: 10.1109/ICHCI58871.2023.10277838.
[3]H. ALawad and S. Kaewunruen, "Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations," in IEEE Access,
vol. 11, pp. 83187-83199, 2023, doi: 10.1109/ACCESS.2023.3264763.
[4]M. Eckel et al., "Implementing a Security Architecture for Safety-Critical Railway Infrastructure," 2021 International Symposium on
Secure and Private Execution Environment Design (SEED), Washington, DC, USA, 2021, pp. 215-226, doi:
10.1109/SEED51797.2021.00033.
[5] T. K. Rajan, S. Karthick, S. Nirmal, S. N. Kumar and S. Senthilmurugan, "IoT Based Remote Surveillance For Animal Tracking Near
Railway Tracks," 2023 International Conference on Networking and Communications (ICNWC), Chennai, India, 2023, pp. 1-7, doi:
10.1109/ICNWC57852.2023.10127346.
[6]J. Zhang, J. Fan, J. Zhang, D. W. K. Ng, Q. Sun and B. Ai, "Performance Analysis and Optimization of NOMA-Based Cell-Free Massive
MIMO for IoT," in IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9625-9639, 15 June15, 2022, doi: 10.1109/JIOT.2021.3130026.
Slide 7](https://image.slidesharecdn.com/rtppt-240118113427-193858ef/85/railway-risk-assessment-in-IOT-based-systems-12-320.jpg)
railway risk assessment in IOT based systems
- 1. EAST WEST INSTITUTE OF TECHNOLOGY BENGALURU-560091 (Affiliated to Visvesvaraya Technological University, Belgaum, Karnataka) BACHELOR OF ENGINEERING IN COMPUTER SCIENCE & ENGINEERING Submitted by, Under the guidance of HARSHITHA M B 1EW20CS050 Prof. SHASHIKALA A B SHEETAL 1EW20CS120 Assistant Professor, SINDHU T 1EW20CS127 Dept. of CSE, EWIT SONU M K 1EW20CS132 EAST WEST INSTITUTE OF TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING No. 63, Off Magadi Road, Vishwaneedam Post Bengaluru–560091 2023-2024 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING PROJECT PHASE-1 SEMINAR ON TITLE “IOT Based Railway Track Faults and Disaster Detection , Checking for Platform Availability”
- 2. AGENDA 1. Introduction 2. Literature Survey( Existing System and Drawbacks of in detail) 3. Problem statement 4 .Objectives 5. System Requirements 6. Applications 7. References 8. Conclusion Slide 1
- 3. INTRODUCTION We know that the railways are the most convenient and cheapest mode of transportation because of its capability, speed and safety. Indian Railways are the largest railway in Asia and the second largest network in the world. The small improvement in this sector will lead to a great development in the country. Due to its huge size, there is a system to monitor and maintain the rails properly and the poor maintenance will create accidents in the rails. . Using the cameras, the presence of creatures can be easily identified and thus the accidents can be prevented. The system contains details of train, loco-pilot, alert system and camera. In the proposed system, the images were captured using the camera and recognized using the process of image processing. If it detects an object in the image, then another image will also be captured within fractions of seconds and again the processing takes place. Both the images will be then compared and if it detects the image in both images, then the alert message will be immediately created by the application and send to loco-pilot and also to nearby control room.
- 4. LITERATURE SURVEY SL NO TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES 1 Railway Track Fault Detection using Deep Neural Networks. S.Rakshith , Sandeep B S, Eliza Femi, Sherly S. 2022 This paper describes the IR transmitter and receiver total station for railway track geometry surveying system. The defect information can be wirelessly transferred to railway safety management centre using a GSM module The defect information which includes GPS value is wirelessly transmitted Satisfactory response. Implemented model were of greater cost 2 Implementation of Railway Accident Judgment Criteria Optimization Based on Data Shulin Liu, Zhenyu Quan, Zihan Jin. 2023 EU railway accident data as the main line to complete the whole process of research and analysis of data collection, data cleaning, data processing, data The optical sensor is used to detect the crack in the railway track Inaccurate. Eddy current influction .
- 5. LITERATURE SURVEY 5 SL NO TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES 3 Railway Track Crack Detection & Obstacle Detection System Sagar Kotkondawar, Samyak Meshram, Abhishek Rangari, Dr. J.P.Sathe, 2022 . This automated system can detect cracks developed on the outside as well as inside thereby avoiding derailment of trains which results in a huge number of fatalities every year a low cost, at the same time an accurate automated system to detect all kinds of the crack present in the railway track compulsary need of a wifi module to send or detect faults. 4 An Experimental Study on the Railway Track Surface Fault Detection with Automation Noorain Mukhtiar, Komal Khuwaja, Danut Pavel Tocut, Ion Tarca 2023 This research proposes an automated approach for real-time surface fault identification of railway tracks by employing intelligent Micro- electromechanical sensors for data acquisition and real-time data processing capabilities, and compatibility with existing railway systems high cost
- 6. LITERATURE SURVEY SL NO TITLE AUTHORS YEAR OBJECTIVES ADVANTAGES DISADVANTAGES 5 Fault prediction of track circuit compensation capacitor based on MFO-LSTM Hongli Gao, Jianqiang Shi, Chengqi Bao, Peng Li, Guangwu Chen . 2023 This automated system can detect cracks developed on the outside as well as inside using MFO-LSTM analysis thereby avoiding derailment of trains which results in a huge number of fatalities every year •a low cost, at the same time an accurate automated system to detect all kinds of the crack present in the railway track compulsary need of a wifi module to send or detect faults
- 7. PROBLEM STATEMENT • Input: • This project has both hardware and software part. • Hardware model will detect track crack, fire and platform availability using sensors. • Web application will get data from hardware model and display it. • Phase one has implementation of hardware model. • Process: • The principle involved in checking platform availability using IR sensors. • Hardware model will send data of platform availability to web application using zigbee transmitter when IR sensor detected. • System will detect fire using fire sensor. • System will detect the crack on track using IR sensors. • Output: • The System will get data from hardware using zigbee and display it on user interface. • System will turn on sprinkle and engine detachment if fire detected. • Engine will stop when crack detected on track. • System will send alerts to controller continuously.
- 8. Slide 5 OBJECTIVES The main aim of the proposed system is to detect the track cracks and platform availability to avoid much accidents. The defect in crack can be found out easily and the preventive measures will be taken immediately. To detect fire and automatic engine detachment. To update the platform availability. • Fire detection • Sprinklers during fire attack. • Automatic engine detachment due to catch of fire. • Crack detection • Platform availability • Alert
- 9. SYSTEM REQUIREMENTS Hardware requirements: Arduino Uno Liquid Crystal Display IR Sensor Motor Driver [L293D] DC Motor Nodemcu [ESP8266] Fire Sensor Ultrasonic Sensor Relay Slide 6
- 10. SYSTEM REQUIREMENTS Software requirement: Arduino IDE Embedded C 10
- 11. APPLICATIONS Platform availability: The principle involved in checking platform availability is when the object is detected the light does not get reflected to IR sensor and the train stops. Here two IR sensors are placed on all platform and a message “PLATFORM 1 AVAIALBLE” OR “PLATFORM 2 AVAILABLE” OR “ALL PLATFORM AVAILABLE” OR “PLATFORM NOT AVAILABLE” is displayed on the LCD. Track Crack detection: This proposed model works on a simple principle i.e. the bot will move on the railway track continuously and immediately the IR sensors gets the input signal low. It will stops the bot and sends the exact location to the server via internet. Python code is return in such a manner that it gives the data value in latitude and longitude form. Fire detection: A fire sensor is placed in each compartment. When the flame is sensed, the sensor will alert the controller and relay will be set high and sprinkler will be activated.. Another motor (Motor 3) is used to detach the compartment to prevent fire from spreading. The message "FIRE" and "HELP" will be displayed on the LCD. Buzzer is used to alert the passengers about the fire. 11
- 12. REFERENCES [1]H. Gao, J. Shi, C. Bao, P. Li and G. Chen, "Fault prediction of track circuit compensation capacitor based on MFO-LSTM," 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China, 2023, pp. 1-5, doi: 10.1109/SAFEPROCESS58597.2023.10295732. [2] M. Xiao and B. Liu, "Fault Diagnosis of Jointless Track Circuit Based on Adaptive Noise Completely Integrated Empirical Modal Decomposition and Deep Belief Network," 2023 4th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), Guangzhou, China, 2023, pp. 64-68, doi: 10.1109/ICHCI58871.2023.10277838. [3]H. ALawad and S. Kaewunruen, "Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations," in IEEE Access, vol. 11, pp. 83187-83199, 2023, doi: 10.1109/ACCESS.2023.3264763. [4]M. Eckel et al., "Implementing a Security Architecture for Safety-Critical Railway Infrastructure," 2021 International Symposium on Secure and Private Execution Environment Design (SEED), Washington, DC, USA, 2021, pp. 215-226, doi: 10.1109/SEED51797.2021.00033. [5] T. K. Rajan, S. Karthick, S. Nirmal, S. N. Kumar and S. Senthilmurugan, "IoT Based Remote Surveillance For Animal Tracking Near Railway Tracks," 2023 International Conference on Networking and Communications (ICNWC), Chennai, India, 2023, pp. 1-7, doi: 10.1109/ICNWC57852.2023.10127346. [6]J. Zhang, J. Fan, J. Zhang, D. W. K. Ng, Q. Sun and B. Ai, "Performance Analysis and Optimization of NOMA-Based Cell-Free Massive MIMO for IoT," in IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9625-9639, 15 June15, 2022, doi: 10.1109/JIOT.2021.3130026. Slide 7
- 13. CONCLUSION The proposed system for creature detection was used for the creature detection on the rails. The proposed system was placed in the accident prone areas where the accidents occur due to the wild animals crossing the rail, vehicle accidents, falling down of trees etc. will be monitored. This system provided the real time image using the image processing technology. According to the system, we were verified the system performance in real condition. The present state of the train and the objects were identified using the proposed image processing algorithms. This information will be helpful for the loco-pilot to stop the train and avoid accidents that harm the creature in the track. 13
- 14. THANK YOU 14