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ROBUST RAILWAY CRACK DETECTION
Under the guidance of Ms.V.S.Chitra, Assistant Professor, SIT Presented by R.P.Kirthigadevi ( ) R.Preethi ( ) V.Vishnu Priya ( )
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ABSTRACT Automatic crack detection.
Reduces heavy loss of life and property. Cost effective solution. Low cost sensors. Error location detected.
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OVERVIEW Introduction Existing method Proposed method Tool requirement
Block diagram Implementation Future Enhancement Work plan Conclusion Hardware snapshots
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INTRODUCTION Railway problems are detected and reduced.
Proposes roubustness solution to crack detection. Sensor based crack detector. Implementation for several applications.
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EXISTING METHOD Manual detection . High cost
Track crack detection only possible.
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PROPOSED METHOD Low power consumption. Less analysis time.
Obstacle & flood detection in single system. Exact error location can be detected . Indication through GPS & GSM.
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HARDWARE REQUIREMENTS
ATMEL Micro controller SIM900 GSM module IWAVE GPS module MAX232 IR SENSORS FLOOD SENSOR OBSTRACLE SENSOR
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SOFTWARE REQUIREMENTS
Keil c IDE Embedded c
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CRACK DETECTION UNIT ATMEL CPU LCD DISPLAY GSM MODEM RS232 GPS module
IR TX GPS module SCU IR RX Flood sensor IR RX IR TX Obstacle detector
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IMPLEMENTATION GPS IR Sensor
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Obstacle Sensor
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FUTURE ENHANCEMENT Regularly checked by fully automatic vehicle.
Separate sensors are not required. Obstacle differentiation can be implemented.
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WORK PLAN January 1st week - Discussion of the project
January 3rd week - Preparation for 1st review. February 1st week-Required components are collected February 3rd week-Literature review discussed. March 4th week – Demo of the project.
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CONCLUSION proposed system reduces manpower. Cost effectiveness.
Increase protection.
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SNAPSHOTS
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DISPLAY
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OUTPUT
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REFERENCES Introduction To Embedded C- N.Mathivannan
“Basic Of Embedded System”- Kenithayala Detection Of Cracks And Railway Collision Avoidance System, S.Ramesh Rail Crack Detection: An Infrared Approach To In-service Track Monitoring,richard J. Greene,john R. Yates, Eann A. Patterson
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Queries ?
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Thank You
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