To improve the availability and reduce the overhaul and maintenance (O&M) costs of gas turbine propulsion systems, the gas turbine industry is moving from scheduled maintenance to condition-based predictive maintenance. Gas turbine diagnostics and prognostics has been progressed quickly in recent years and are crucial technologies to predict the health of propulsion systems and support the predictive maintenance. However, current technologies are relatively slow and not capable enough to provide quick diagnostic and prognostic predictions for real time applications. With the rapid development of artificial intelligence (AI) nowadays, it has become possible to develop a fast-response AI-based condition monitoring system for gas turbine engines.

The objective of the project is to develop AI-based diagnostic and prognostic technologies and a digital-twin system for gas path condition monitoring to support condition-based predictive maintenance of a specified gas turbine engine.

The project will be fully funded by a research institution with a focus on aerospace technology.

Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of performance diagnostics and prognostics and creep and LCF life consumption monitoring to support condition-based predictive maintenance for aircraft gas turbine engines.

Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and creep and LCF lifing methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can be used effectively as a performance digital twin to generate high-quality engine performance models and produce required training data for the proposed project. This could be a good starting point for the project.

The objective of the proposed research project will be the development of digital twin technology for the diagnostic and prognostic predictions of engine degradation and remaining useful life of critical components of gas turbine engines. A novel AI-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of engine health and remaining useful life of a specified aircraft engine.

Non-confidential operational gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors, combustor and turbines. It is aimed to make a step forward in gas turbine diagnostics and prognostics in terms of prediction accuracy, real-time applicability and computational efficiency. As for the AI perspective, novel models will be developed in order to save time from the training process and improve the robustness and accuracy of the predictions.

The Centre for Propulsion and Thermal Power Engineering has a key focus and a proven track record on gas turbine performance, gas path diagnostics and prognostics, lifing, etc., which have been built up over the last half century. This provides a unique capability to assist researchers and engineers in gas turbine performance and health monitoring in gas turbine and power generation industry.

The history of gas turbine performance engineering at Cranfield dates back to 1946 and the foundation of the Institution. The Centre for Propulsion and Thermal Power Engineering contributes and focuses on gas turbines for aerospace, marine and power generation applications, the sectors where Europe and Britain are world leaders and major exporters. This high technology global industry is worth more than £30 billion per annum. Current challenges are arising from the need to address environmental issues, more cost-effective operations, maintenance and overhauls and the changing economic climate. These challenges have created an environment where a large return can be accrued from an investment in gas turbines and related power system research and education.

It is expected the research will generate new AI-based methods and knowledge for more advanced gas turbine health and life monitoring. The new knowledge will be very useful to guide condition-based operation and predictive maintenance of future gas turbine engines.

The student will be based within the School of Aerospace, Transport and Manufacturing. Cranfield is a wholly postgraduate university and there are a wide range of MSc and Professional Development Short Courses throughout the year. The student may have opportunities to access some of the MSc courses and CPD short courses relevant to the research and attend and publish papers in international conferences.

Cranfield operates a substantial Doctoral Researchers Core Development programme (DRCD) for its research students. This programme provides a generic structured training programme which is constructed to support the researcher as the PhD progresses with specific courses aimed at the different phases of a PhD. For example, the programme includes aspects such as research methods, technical report writing, presentation skills, data management, leadership skills, professional development planning, intellectual property, publishing, etc. Such knowledge, skills and capability will enhance the student's employment opportunities in both academic institutions and industry.

This is an exciting opportunity for a suitable candidate where he or she will be exposed to the latest technology, learn from experts working in the area and prepare for an exciting career in either academia or industry.

The successful candidate will have the opportunities to visit the sponsor annually and attend an international conference to present papers, both funded by the project.

At a glance

  • Application deadline02 Aug 2023
  • Award type(s)PhD
  • Start date02 Oct 2023
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberSATM366

Entry requirements

Applicants should have either a UK or UK-equivalent first or second-class honours degree in mechanical engineering, aerospace engineering or a relevant area. An MSc degree and/or experience in thermodynamics, gas turbine performance, gas path diagnostics and prognostics, Artificial Intelligence, and computer programming will be an advantage.


A fully funded PhD studentship for three years will be available for both UK and non-UK candidates.

About the sponsor

The project is sponsored by Cranfield University and a research institution with a focus on aerospace technology.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

For further information please contact:

Name: Dr Yiguang Li

If you are eligible to apply for this studentship, please complete the online application form.