Applications are invited for a Ph.D. degree in the area of IoT based Maintenance and Asset Management. The research would focus on developing and leveraging AI optimised IoT sensory systems for feature engineering, prognostics, and data analytics in existing state-of-the-art as well as the next-generation systems, such as cyber physical (CPS), all-electric aircraft, smart grid, and autonomous vehicles. Read more Read less

With the developments in IoT (Internet of Things), sensor data is streamed wirelessly from systems, sub-systems, or assets, to remote servers in the cloud. In this manner, all data relevant to health estimation (e.g., environmental conditions, maintenance, and operating data) are available for health monitoring and prognostic assessment. The sharing of information across assets and platforms enables the development of a complete operating picture and the flexibility to assess and manage new and even previously unknown maintenance and management risks. This, however, becomes intensively complex to handle and arrive at optimal asset maintenance and management decisions in a shorter period of time with higher accuracy, especially in mission-critical environments. Cognitive/AI optimised approach for IoT based maintenance and asset management holds the key to increasing efficiency of such complex IoT structures.


This research project aims at designing and developing Cognitive/AI optimised IoT sensory systems for feature engineering, prognostics, and data analytics that augment the efficiency of existing IoT based maintenance and asset management activities. Novel machine learning algorithms/deep neural networks may be, therefore, developed and validated to support mission-critical environments for their optimum maintenance and management.   


Cranfield is a unique learning environment with world-class programmes, unrivaled facilities, and close links with business, industry, and governments, all combining to attract the best students and teaching staff from around the world. In 2014, 81% of research at Cranfield was rated as world-leading or internationally excellent in the Research Excellence Framework (REF).


The Integrated Vehicle Health Management (IVHM) Centre is in its 12th year of operation. Founded by Boeing and a number of aerospace partners (BAE Systems, Rolls-Royce, Meggitt, and Thales) in 2008, it has grown to perform work in sectors such as transport, aerospace, and manufacturing. The Centre integrates a multidisciplinary research effort to develop cost-effective component and system health management technologies capable of supporting ground and on-board applications of high-value, high-complexity systems. IVHM Centre is a member of Digital Aviation Research and Technology Centre (DARTeC), which focuses its researchon aircraft maintenance, connected systems, unmanned traffic management, seamless journey, distributed airport/airspace management, and conscious aircraft. Research England, Thales, Saab, Aveillant IVHM Centre, and Boxarr are some of the prominent members of DARTec. IVHM Centre also works in close collaboration with Aerospace Integration Research Centre (AIRC), founded in partnership with Airbus and Rolls-Royce. The potential Ph.D. candidate will have access to the facilities held by AIRC and DARTeC in addition to having interactive sessions with experts at AIRC and DARTeC.  


The successful culmination of this project envisages the availability of an efficient and cognitive IoT for asset maintenance and management system that determines the precise timing of all applicable maintenance actions to ensure the asset will be available when needed throughout its operational service life. 


The project will provide active collaboration and exchange of ideas and knowledge with key stakeholders within different centres of the Cranfield university and industrial partners in the aircraft industry. The paraphernalia of AI and Machine Learning based applications within IVHM centre and across other research centres would be helpful for the potential researcher in acquiring essential knowledge and building skills (e.g., AI algorithms’ formulation) required for this specific research project. The IVHM Centre encourages and supports ample opportunities for disseminating individual research through reputed journals and presenting papers in high profile and well-known IEEE conferences within the UK and across the globe. More significantly, the potential candidate will have the opportunity to present his research work during quarterly technical reviews to the wider research community from within the university and the industrial partners. It also provides a networking platform for promising researchers to lay the foundations of their professional relationships with key representatives from various companies.


Upon successful completion of the project, the potential candidate will be able to carry out research activities independently and more vigorously. This research will be formative for the potential candidate in building his/her analytical logic and algorithm craftsmanship. The understanding of the essence and application of IoT for maintenance and asset management would broaden the employability scope appreciably.     

At a glance

  • Application deadline30 Apr 2021
  • Award type(s)PhD
  • Start dateAs soon as possible
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberSATM192

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit …(a) graduate and post-graduate students with a degree in engineering (preferably in electrical/electronic/mechanical/aerospace/or computation), data science, or any other related physical sciences subject, and (b) Researchers and Engineers with a background/interest in IoT and asset management systems. Ph.D. candidates must have software programming skills (intermediate to advanced level) and familiarity with AI and Machine Learning methods. Above all, research aspirants with an innovative approach, high motivation, and willingness to learn are welcomed.


This is a self-funded opportunity. 

About the sponsor

This is a self-funded opportunity.

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: Suresh Perinpanayagam
T: (0) 1234 750111 Ext: 2377       

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