Mr. Jathursajan Kanakasuntharam

Mr. Jathursajan Kanakasuntharam

Temporary Lecturer

Contact

  • jatursajan.k@nsbm.ac.lk
  • +94 11 544 5000 (ext 1345)
  • Department of Mechatronic and Industry Engineering
  • Faculty of Engineering
  • Google Scholar
  • Linkedin

Profile

Mr. Jathursajan Kanakasuntharam obtained a Bachelor of engineering technology (Hons) in mechatronics with first class from the University of Sri Jayewardenepura, Sri Lanka. He holds the highest GPA in the Material and Mechanical Technology Department with three dean lists. Moreover, he was the Island’s first rank holder of the A/L examination in the Engineering Technology stream in 2016. His research interests lie in condition monitoring, machine learning, and audio processing. He had been working as an academic demonstrator at the University of Sri Jayewardenepura before joining as a temporary lecturer at NSBM Green University.  

Research Interest

Mr. Jathursajan Kanakasuntharam obtained a Bachelor of engineering technology (Hons) in mechatronics with first class from the University of Sri Jayewardenepura, Sri Lanka. He holds the highest GPA in the Material and Mechanical Technology Department with three dean lists. Moreover, he was the Island’s first rank holder of the A/L examination in the Engineering Technology stream in 2016. His research interests lie in condition monitoring, machine learning, and audio processing. He had been working as an academic demonstrator at the University of Sri Jayewardenepura before joining as a temporary lecturer at NSBM Green University.  

Journal Publications

  1. Jathursajan, K. ., & Wijethunge, A. (2022). Diagnosing Localized and Distributed Bearing Faults by Bearing Noise Signal Using Machine Learning and Kurstogram. Advances in Technology, 2(2), 139–150. https://doi.org/10.31357/ait.v2i2.5475 

Conference (Full Paper) Publications

  1. Jathursajan and A. Wijethunge, “Diagnosing localized and distributed faults of rolling bearing using Kurstogram and machine learning algorithms using bearings audio signal in comparison with vibration signal”, MERConn2022, 2022, pp. 1-6. 10.1109/MERCon55799.2022.9906173
  2. Jathursajan and A. Wijethunge, “Raspberry pi-based bearing fault diagnosis by bearing audio and vibration signal via cost-effective accelerometer”, WIESymp-2022, 2022.