Predictive Maintenance (PdM) is one of the maintenance strategies that has attracted the attention of businesses in this Industry 4.0 era. Even though the PdM strategy has been introduced more than two decades ago, its adoption and implementation in the industry have been rapidly accelerated in the last few years along with the booming of digital technologies. 

One of the key elements for a successful implementation of the PdM strategy is the usage of technologies that can effectively convert relevant measurement data into actionable information, such as the health condition, and/or the remaining useful life of critical assets.

Currently, Artificial Intelligence (AI) based big data analytics tools have been massively developed in the research community to address this challenge. These AI-based analytics tools are data-driven and black-box, so the interpretation of how predictions are made by such techniques is still an open problem. Although these techniques have been successfully demonstrated in laboratory environments, however, the barrier to deploying such pure-data-driven black-box techniques in the industry is still high.

One of the main reasons is that the performance of such techniques highly depends on a large amount of good-quality data. Unfortunately, the availability of good-quality data is typically limited for high-value critical assets.

To remedy this gap, this PhD project will focus on developing, evaluating, and demonstrating domain knowledge-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique will be first explored, tailored, and extended into the PdM context of high-value critical assets. It is expected that combining the domain knowledge and the existing data analytics tools will help deploy these technologies in the industry context without the need for a large amount of datasets.

The successful candidate will have the opportunity to work with experts in the data analytics, condition monitoring, prognostics, and health management field, as well as be part of our strong and dynamic research centre at Cranfield University.

At a glance

  • Application deadline26 Apr 2023
  • Award type(s)PhD
  • Start date05 Jun 2023
  • Duration of award3 years
  • EligibilityUK, EU, Rest of World
  • Reference numberSATM347


Dr Agusmian Ompusunggu

Entry requirements

  • Should have a minimum of a first or second-class UK honours degree or equivalent in a related discipline (e.g., mechanical, electrical, computer science, manufacturing, aerospace, and automotive) with a minimum 65% mark in the Project element or equivalent with a minimum 65% overall module average.
  • Should have the potential to engage in innovative research and to complete the PhD within a three-year period of study.
  • Must have a minimum of English language proficiency (IELTS overall minimum score of 6.5).

Also, the candidate is expected to:

  • Have excellent analytical, reporting and communication skills
  • Be self-motivated, independent and a team player
  • Be genuine enthusiasm about the subject and technology
  • Have the willingness to publish research findings in international journals


This is a self-funded opportunity so the student would need to source their own funding. However, a bursary might be considered for an exceptional candidate. The application is open to UK and international students.

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

Before completing the application documentation, please contact for an initial informal discussion about this opportunity.

Please include the keyword PhD Studentship-Self Funding in the subject field.