Gishan joined the University of Cambridge in 2017 to pursue an EPSRC funded PhD in Predictive Maintenance and Engineering. In his PhD, he is developing a methodology for predicting equipment failure under the conditions of limited failure data availability and showing its impact on the predictive maintenance of industrial equipment. Whilst Dr Ajith Parlikad is Gishan's PhD supervisor, Prof Mark Girolami and Prof Duncan McFarlane are his PhD advisors.
Before starting the PhD, he completed a master’s degree in Computer Science and Artificial Intelligence at the University of Nottingham. He completed internships at Roller Agency and JP Morgan during his bachelor’s degree at the University of Nottingham which was also in Computer Science and Artificial Intelligence.
With regards to work experience, he has over 7 years of industrial working experience in Software Developer, Software Solutions Architect and Head of Software Development roles and involved in designing and developing software solutions for companies such as ERT, AstraZeneca, GSK, Novartis, Quorn, MYZONE and HelpAge International.
Gishan is the recipient of IEEE 2019 International Prognostics Best Paper Award and 2018 Fitzwilliam College, Cambridge Senior Scholarship.
- Predictive maintenance.
- Industrial equipment prognostics.
- Limited failure data availability.
- Generative modeling.
- Disentangled representation learning.
- Machine learning.
- Information theory.
- Predictive maintenance under the conditions of limited data availability (PhD research).
- British Telecom (BT) multi-service edge router prognostics and customer broadband service fault prediction in collaboration with BT (PhD research case study).
- Air purge valve failure prediction in Scania trucks in collaboration with Scania Commercial Vehicles and Stockholm University (PhD research case study).
- Digital twin research platform development in collaboration with Centre for Digital Built Britain, Bentley Systems, Topcon, GeoSLAM and RedBite (extracurricular research activity).
- Don Ranasinghe, G., and Parlikad, A. Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability, 2019 IEEE International Conference on Prognostics and Health Management, San Francisco (2019).
Peer reviewed journal articles
- A Repairable System Subjected to Hierarchical Competing Risks: Modeling and Applications, Access Id: 2019-31392, IEEE Access Journal, 30/08/2019: https://publons.com/researcher/3095286/gishan-don-ranasinghe/
- Best Paper Award, IEEE International Conference on Prognostics and Health Management, San Francisco, 2019.
- Cambridge College Senior Scholarship, Fitzwilliam College, University of Cambridge, 2018.
- Cambridge PhD Studentship, Engineering and Physical Sciences Research Council, 2017.