Departments
Admin

THOMAS JOSEPH

Assistant Professor

Areas of Specialization

Computer Aided Design.
Areas of Interest

Applications of Artificial Intelligence in Mechanical Systems, Tribology.

Educational Summary


Course University Year
B. Tech (Mechanical Engineering) University of Kerala 2011
M.E (computer Aided Design) Anna University 2014
PhD( Pursuing) APJ Abdul Kalam Technological University

Experience


From Date To Date Institution Name Designation
2023-02-01 0000-00-00 Jyothi Engineering college Asst. Professor
2024-08-31 2023-01-31 NSS College of Engineering, Palakkad Ass. Professor(Adhoc)
2014-08-10 2022-08-30 Jawaharlal College of Engineering and Technology, Lakkidi, Ottapalam Asst. Professor
2011-07-26 2012-07-26 Jawaharlal College of Engineering and Technology, Lakkidi, Ottapalam Lecturer

Achievements


Achievement Year
Receives financial assistance of Rs:9000/- from Kerala State Council for Science, Technology and Environment for the project titled “Alcohol Detection and Locking System” 2020
An Indian Patent “An Artificially Intelligent Computational Fluid Dynamics Method for Smart Valves” 2022

Papers Published


Joseph, T., Keerthi Krishnan, K., Sudeep, U. (2022). A Comparative Study of Rolling Bearing Fault Classification Using CWT-CNN and STFT-CNN Methods. 12th ICIT conference, Tribology society of India and IIT Delhi, 11/12/2022-14/12/2022; Also published In: Tribology for Energy, Environment and Society. ICOIT 2022. Lecture Notes in Mechanical Engineering, Springer nature.

Conferences / Workshop / FDPs Attended


Presented a topic “A COMPARATIVE STUDY OF ROLLING BEARING FAULT CLASSIFICATION USING CWT-CNN AND STFT-CNN METHODS “a conference conducted by tribological society of India and IIT Delhi named as India Trib-2022.
Presented a topic “A REVIEW ON ARTIFICIAL INTELLIGENCE BASED EARLY FAULT DETECTION OF ROLLING BEARINGS USING MODAL DECOMPOSITION OF VIBRATION DATA” Second Virtual International Conference on Advanced Technologies and Research in Mechanical Engineering, ISBN: 978-81-956814-0-2. Organized by Department of Mechanical Engineering MEA Engineering College.
Presented a topic Vibrational Analysis of Tapered Core Sandwich Beam at National level Conference on Recent Advances in Engineering and Technology Conducted by ISTE and Gharda Institute of technology Ratnagiri Maharashtra. at 15,16-11-2013
Attended 5 Days training programme, Mission 10x on High Impact Teaching Skills Conducted by Wipro from 19-03-2012 to 23-03-2012
Attended 7 Days ETI Training for NSS Program Officers from 15-03-2021 to 21-03-2021 at Empaneled training Institute for NSS, university of Calicut.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on "Artificial Intelligence" from 30-04-2020 to 04-05-2020 at College of Technology and Engineering, Udaipur.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on "3D Printing" from 2020-11-9 to 2020-11-13 at MBM ENGINEERING COLLEGE JODHPUR.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on "Waste Technology" from 2020-11-2 to 2020-11-6 at Manipal University Jaipur.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on "Data Analysis and Machine Learning using Python" from 2021-07-12 to 2021-07-16 at School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on "Operations Management" from 16/08/2021 to 20/08/2021 at Goa College of Engineering.
Participated & completed successfully AICTE Training and Learning (ATAL) Academy Online FDP on " Conceptual and Practical Knowledge Sharing and Training Sessions on Computational Fluid Dynamics" from 2021-6-14 to 2021-6-18 at Nehru Institute of Engineering and Technology.

Projects Guided


Projects Name Details Year
Comparative study of Machine learning and Deep learning classifiers for rollimg bearing fault diagnosis 2023-2024
Intelligent fault identification and classification of rolling bearing faults using machine learning techniques from frequency domain features 2023-2024