Design and Development of Prescriptive Maintenance Framework for Digital Manufacturing

   School of Computing, Engineering & the Built Environment

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project


Please use reference number SCEBE-21SF-DDPMFDM-Jain


The fourth industrial revolution prompted a paradigm shift from predictive to prescriptive maintenance. Accordingly, this PhD project deals with demystifying the prescriptive maintenance framework and enabling this for digital manufacturing. More specifically, the application of this framework will be demonstrated in a use case for machining systems in metal manufacturing. For instance, intelligent maintenance of machining assets such as CNC machine tools, and cutting tools. Nevertheless, the framework will be generalised and be utilized in domains outside of manufacturing including automobiles and energy.

Prescriptive maintenance advances the prevailing predictive maintenance strategy by not only predicting failure events but also recommending actions to take. For instance, compute the effects of changing the operating profiles on the time-to-failure. Specifically, prescriptive maintenance combines prognostics and prescriptive analytics to not only understand and reason out past events but also to anticipate the chances of future events and the probable effects of each decision alternative on the physical space and accompanying business processes. It is recognized that scarce studies are accessible, associating prescriptive maintenance in the digital manufacturing environment. As a result, the opportunities for prescriptive maintenance are endless, and breakthroughs are essential.


In this research study, the candidate is expected to:

-       Conduct a literature review on the existing prescriptive maintenance approaches and recognize the challenges and gaps in the context of positioning in a digital manufacturing environment.

-       Investigate domain knowledge, prognostics, prescriptive analytics, simulations, semantic reasoning, and ML/AI for the development of a prescriptive maintenance framework.

-       Design an innovative tool that provides multiple scenarios and simulations without experiencing each one in real life.

-       Develop and validate an industry-oriented framework for prescriptive maintenance targeting deployment in a real-world digital manufacturing environment.

The candidate is expected to write a detailed proposal not more than 2000 words clearly stating how any of the points above can be executed.


The successful candidate has:

·       A Master’s Degree in relevant Engineering and Science subjects with skills in the development of maintenance planning models and integration of data analytics solutions.

·       Knowledge of machine learning techniques.

·       Interest in stochastic modelling and simulation.

·       Strong mathematical, analytical, and programming skills using Python, MATLAB, and C++.

·       Excellent communication skills in spoken and written English, and teamwork skills.

·       Creativity, positive attitude, and perseverance.


A bench fee of £4000 is required for attendance of relevant conferences and training.

How to Apply

This project is available as a 3 years full-time or 6 years part-time PhD study programme. 

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 our Application Process page, choosing Mechanical Engineering and their preferred intake date.  

Please send any other enquires regarding your application to:

Computer Science (8) Engineering (12)

Funding Notes

Applicants are expected to find external funding sources to cover the tuition fees and living expenses. Alumni and International students new to GCU who are self-funding are eligible for fee discounts. See more on fees and funding. View Website
A bench fee of £4000 is required for attendance of relevant conferences and training.


For further information, please contact the supervisory team via the contact details below. Please note, this is a query email only and will not be considered as an official application.
Director of Studies
Name: Dr. Amit Kumar Jain
GCU Research Online URL:
2nd Supervisor Name: Prof. Babakalli Alkali
GCU Research Online URL:

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