Knowing when systems need to be maintained is of great importance to companies operating high-value assets. Further, to know ahead of time, with plenty of time to optimize the maintenance and cause the least amount of disruption to the operation, would be highly desirable. This is the goal of prognostics. Physical systems/components’ degradation phenomenon can be captured in prognostics models. The prognostic capability focuses on predicting the future performance of a system, specifically the time at which the system no long performs its desired functionality or its time to failure. This challenge becomes even greater for assets with long life-cycles and sometimes-inaccessible nature (particularly underground assets) as this can easily complicate the ability to understand the likely timing of deteriorations or of critical asset failures. Furthermore, for real applications, the current prognostic models are imperfect, resulting in ineffective RUL calculations. Wrong estimations of the remaining useful life might have a significant effect on maintenance costs if these activities are primarily based on condition. Hence, a hybrid prognostic approach is aimed at leveraging the advantages of physics based and data driven and contextualized knowledge based approaches to compensate for their limitations. In this research, we aim to tackle the limitations listed above by developing a hybrid prognostics approach combining a mix of Knowledge-based, Data-driven and Physics-based modelling (KDP) of a representative asset.
This PhD project aim is to build on the lessons learnt from projects delivered for industrial customers in the aerospace and transportation industry sectors and to develop a hybrid prognostic approach that integrates knowledge-based, physics-based and data-driven calculations of Remaining Useful Life (RUL) of critical components; It is envisaged that this innovative approach will pave the way for implementing predictive maintenance (PdM) 4.0 for critical assets.
The supervisory team already discussed the project and its potential outcomes with several OEMs of master control valves used in surface X-Mas trees supporting Oil & Gas operations. It is envisaged that a proof of concept at low TRL levels will enable advances in understanding and sensing of degradation at higher TRL levels for such equipment.
Previous experience of analysing digital information and sensory data, data mining, pattern recognition, machine learning, abilities of drawing the right conclusions, and acting on them in the digital or physical world is desirable. An understanding of typical maintenance KPIs, asset design data, maintenance and failure concepts, condition data and contextualize environmental data is also highly disable.
Candidates are requested to submit a more detailed research proposal (of a maximum of 2000 words) on the project area as part of their application.
Research Strategy and Research Profile
Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals, We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments. For more. This project is part of the research activity of the Engineering Simulations and Advanced Manufacturing research Group.
How to Apply
Applicants will normally hold a UK honours degree 2:1 (or equivalent); or a Masters degree in a subject relevant to the research project. Equivalent professional qualifications and any appropriate research experience may be considered. A minimum English language level of IELTS score of 6.5 (or equivalent) with no element below 6.0 is required. Some research disciplines may require higher levels.
Candidates are encouraged to contact the research supervisors for the project before applying. Applicants should complete the online GCU Research Application Form, stating the Project Title and Reference Number (listed above).
Please also attach to the online application, copies of academic qualifications (including IELTS if required), 2 references and any other relevant documentation.
Please send any enquiries regarding your application to: [email protected]
Applicants shortlisted for the PhD project will be contacted for an interview.
For more information on How to apply and the online application form please go to https://www.gcu.ac.uk/research/postgraduateresearchstudy/applicationprocess/
Dr Octavian Niculita [email protected]