PhD Studentship: Railway Rolling Stock Fleet Data Analytics Towards Reliability and Performance Prediction

Supervisors: Prof Babakalli Alkali (Director of Studies), Dr Amit Kumar Jain, Dr Juan Manuel Parrilla Gutierrez

About the Project

Please select/refer to project number SCEBE/22Stu/Alkali in your application, selection list or in any email correspondence.

Aims

In this PhD research project, you will be expected to conduct a comprehensive investigation into the rolling stock fleet in-service, risks, reliability, and operational performance to understand the reasons for the apparent differences in fleet reliability and performance data. A good technical understanding of the mechanical and electrical design configuration of rolling (passenger and freight services) is desirable in this project. You will be expected to explore and conduct investigation of the academic literature specifically within the mechanical, statistical, risk, performance, and reliability domain to determine the state-of-the-art dynamic statistical and other modelling methods using the concept of AI and machine learning approach towards anomaly detection, predictive, prescriptive analytics, and maintenance optimisation.

Conduct multiple criteria decision analysis (MCDA) incorporating AI and Machine Learning techniques and use the ANN concept and time series modeling approach linked to dashboards.

Project information

This PhD research is funded by Siemens and Glasgow Caledonian University.

Candidates applying for this PhD studentship research will get the full PhD funding over 3 plus stipend and a bench fee to attend international conferences. Candidate interested will be expected to write a 1000-word project proposal clearly highlighted how the above project can be executed appropriately to support their application.

Applicant criteria

Candidates applying for this PhD studentship is expected to have moderate technical understanding of mechanical, electrical, and electronic systems in railway systems. Previous experience in mathematical and statistical modelling, computer programming and quantitative research methodology is desirable. 

Funding

The successful candidate will have the benefit of bench fee work £3000 per/annum attached to the project for further training and development.

How to apply

This project is available as a 3 years full-time PhD study programme with expected start date of 1^st October 2023.

Candidates are encouraged to contact the research supervisors for the project before applying.

Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using the Application Process page, choosing a October 2023 Start.

Applicants shortlisted for the PhD project will be contacted for an interview within six weeks from the closing date.

Please send general enquires regarding your application to: researchapplications@gcu.ac.uk

Funding notes

A breakdown of fees and stipend are below:

• £17,688 stipend for student per annum x3
• £16,800 fees per annum (funded by the SCEBE) x3

Total cost of £34,488 per year x3

There will also be an additional bench fee of £3,000 per annum (x3) for training, development and international conference attendance.

References

Candidates are encouraged to contact the research supervisors for the project before applying.

Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using the Application Process page, choosing a October 2023 Start.

Please contact for informal queries:
Name: Professor Babakalli Alkali
Email: babakalli.alkali@gcu.ac.uk

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