Bayesian Deep Learning for High Voltage Condition Monitoring

Background
SCEBE-19-012

To ensure the power system operates safely and reliably, it is essential to monitor and evaluate the health condition of power equipment. Condition monitoring of motors, transformers and generators to diagnose faults allows for preventative maintenance to be undertaken early. This process involves engineers visiting a site to collect signal measurements, which are then taken for manual analysis off site. This type of analysis is costly, time consuming and un-scalable. Recently with developments in the Machine Learning, Internet of Things, Cloud and Edge Computing there has been an increasing demand for online condition monitoring systems, which perform real time analysis.

Aims
The aim of this project is to develop novel Deep Learning and Bayesian based algorithmic techniques for application to High Voltage Condition monitoring systems. The two main aims of the project are the development of improved classification algorithms for fault diagnosis, and the development of predictive analytics based methods for prognosis. The classification stage will involve extending upon our previous work in fault classification using Machine Learning to incorporate and model uncertainty within the predictions of the algorithms using Probabilistic Programming. The prediction stage will exploit and extend upon novel Bayesian Deep Learning based methods for Time Series Prediction. Both project stages will utilize real world data and will aim to develop a computationally efficient solution that will be productized by our industrial partner.

Specifications
The successful applicant will be a Computer Science, Artificial Intelligence, Mathematics or Electrical and Electronic Engineering graduate holding the minimum of a first degree (2:1 or above); or a Masters degree in a subject relevant to the research project. The successful candidate should demonstrate the capability to work across a diversity of technical areas with an inquisitive and problem solving approach and have excellent knowledge and experience of programming and analytical skills.

Research Strategy and Research Profile
Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals (https://www.un.org/sustainabledevelopment/sustainable-development-goals/), We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments. For more (https://www.gcu.ac.uk/research/). This project is part of the research activity of the Research Group – Artificial Intelligence

How to Apply

Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels.

Candidates are encouraged to contact the research supervisors for the project before applying. Applicants should complete the online GCU Research Application Form, stating the Project Title and Reference Number (listed above).

Please also attach to the online application, copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation.

Please send any enquiries regarding your application to: [Email Address Removed] Applicants shortlisted for the PhD project will be contacted for an interview.

For more information on How to apply and the online application form please go to
https://www.gcu.ac.uk/research/postgraduateresearchstudy/applicationprocess/

Professor Gordon Morison [Email Address Removed]