Dr. Preeta Sharan’s research lab is churning out several innovations silently
Dr. Sharan is developing such a system in an especially created lab train model at R&D Centre of The Oxford College of Engineering, Bengaluru which is sponsored by AICTE, New Delhi.
Such System to monitor structural and operational conditions of trains continuously on real-time basis will help in reducing train accidents especially due to mechanical errors, poor track repair infrastructure and bridge / tunnel collapses
By Susmita Saurav
New Delhi, Jan 02, 2022
Bengaluru based Oxford College of Engineering is developing a system to monitor structural health of running Trains. A smart condition monitoring system would allow real-time and continuous monitoring of the structural and operational conditions of trains as well as monitoring of the structural health of Rail tracks and the location, speed and weight of passing Trains of the entire Rail systems.
This system is being developed under the leadership of Dr. Preeta Sharan, a Professor at The Oxford College of Engineering Bengaluru, Karnataka, India who has been involved in R&D, speciality in device development since quite long. Dr. Sharan is developing such a system in an especially created lab train model at R&D Centre of The Oxford College of Engineering, Bengaluru which is sponsored by AICTE, New Delhi.
Development of such a system to monitor structural health of running Trains is very important in country like India which heavily depends on mass transportation system and highly prone to accidents.
According to the NCRB Annual Report 2020, there were 13,018 in 2020. As many as 11,986 railway passengers were killed and 11,127 were injured in these accidents during the past year. The highest numbers of train accidents or 20 per cent of the total were found to be in Maharashtra, while Uttar Pradesh followed up at number two with 12 per cent of the total number of accidents. In 2019, there were 27,987 train accidents in India according to report.
It is important to mention here that out of the 13,018 train accidents reported in 2020, as many as 12,440 of them took place due to the fault of the loco pilot, the person responsible for driving the train and ensuring its proper maintenance during transit. Other reasons include errors on part of the signalman, mechanical errors, poor track repair infrastructure, bridge/tunnel collapse, and the likes.
Thus, developing such a system to monitor structural and operational conditions of trains continuously on real-time basis will help in reducing train accidents especially due to mechanical errors, poor track repair infrastructure and bridge / tunnel collapses.
Dr. Sharan’s lab and team Suchandana Mishra and Deepa is developinga Fiberoptical sensor to study the rail wheel and health monitoring with scaled railway model. This gives feasibility for solving the rail problems remotely as every time field visit is difficult. Focusing on the train basic parameters will help in smooth functioning and managing of trains for betterment of people safety.
A post-doctorate from IIT Kharagpur and being a competent professor with over 23 years of experience in Education, Research, and Student Mentorship, Dr. Preeta Sharan is instrumental in grooming young talent. Her appreciation is now known to international universities and organizationsas many path-breaking innovations resulted from her lab. Here are some of the latest innovations from her lab (https://bit.ly/32Fa385).
Some of these researches are the result of funded research and she played the role of the lead investigator.
Detection of skin cancer:
In the last few years, Deep Learning has been showing superior performance in difficult phases of biomedical image analysis. Skin cancer cases are continuously arising from the past few years, especially melanoma is one of the deadliest diseases. Dr. Preeta Sharan’s team worked on the project and used the deep learning concept. The team has proposed a system, which will be very useful in predicting skin cancer and give an accurate result. This is specifically designed to find whether the skin has melanoma (skin cancer) or is normal. The developed algorithm is trained to predict it better than the existing systems. In this project team has developed an android application-based mobile camera for an easy and efficient way to detect skin cancer with a home base monitor for easy access.