railway risk assessment

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.
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14. THANK YOU
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