This is a very exciting opportunity to develop prognostic and diagnostic techniques for rotating machinery in submarines, using real-world data from existing vessels to inform future capability on next-generation models. Working closely with industrial partners, this collaboration will develop a suite of analytics techniques which will demonstrate improved methods of understanding the health of rotating assets, with the view to moving towards a proactive condition-based health monitoring strategy from the traditional time-based approaches. This will involve experimental design, software development and data science, carrying out research and development of models to predict and diagnose asset health condition. Candidates will be expected to have expertise and track record in all of the areas listed below. Individuals with complementary expertise will be considered, so please do apply even if you only meet some of the criteria:

 

•Condition monitoring and asset management of engineering systems, and subsystems, particularly rotating machines.

•Requirements capture and experimental design.

•Data Science – handling and processing large data sets (experience across multiple domains welcome).

•Artificial Intelligence – including predictive modelling, pattern analysis and recognition.

•Software development and testing experience – ideally in rotational plant, but experience in other areas will also be considered.

 

As a Knowledge Exchange Fellow, you will engage as an independent knowledge exchange professional in individual and collaborative knowledge exchange projects, establishing a distinctive programme of area of knowledge exchange and generating interest through engagement with industry and professional bodies. You will apply as Principal- or Co-Investigator, to appropriate external organisations for knowledge exchange funding and manage projects secured. You will write up reports, often as lead author, for external organisations, and further write up findings for additional dissemination (e.g. professional publications or peer review journal publication) as appropriate.

To be considered for the role, you will have a good honours degree and PhD / higher degree (or equivalent professional experience) in appropriate discipline. You will have a personal track record in carrying out knowledge exchange projects and a demonstrable track record in developing high quality knowledge exchange proposals and playing a leading role in attracting knowledge exchange funding. The successful candidate will have knowledge exchange interests which are consistent with the strategic direction of the Department/School and will have the ability to plan and organise knowledge exchange programmes, and to pull together teams of academic professional staff and others as appropriate, to ensure project delivery for the client and benefits to the University.

Whilst not essential for the role, applications are welcomed from candidates with: membership of relevant Chartered/professional bodies (including the Higher Education Academy), experience of multi/inter-disciplinary research, experience of student assessment activities and/or a track record in knowledge exchange related activities.

The primary project the researcher will work on will be to join a team to develop and deploy intelligent decision support software for enhanced operation and predictive maintenance of pumps and rotating assets within the UK’s submarine capability. By leveraging advances in machine learning and artificial intelligence, this will facilitate the move away from a reactive time-based approach towards a data-driven, predictive maintenance strategy for safer, secure and more cost-effective support. The project has three main aims, namely:

•Completion of a detailed review and assessment of the data and experience associated with existing rotating plant equipment, including current approaches to maintenance (run to failure, time-based, and predictive), existing models and processes used to inform current asset health, data captured (both online through condition monitoring, as well as operational parameters, and offline during routine inspection and maintenance activities), platforms or technologies employed to manage and analyse the data. This will provide the baseline data sets and analytics approaches currently employed 2

•Development of a proof-of-concept tool, which will provide a single point of access for historic data and records, analyses tools and a RAG-type indicator showing the current estimate of each rotating asset’s condition. This will be an iterative process, where hands on visualisations targeted at key end users will ensure the system is able to deliver its core functions, and modular in nature to allow additional functionality to be deployed using a common platform

•Development of a suite of demonstration analytics, accessible through the visualisation tool, and providing support for typical through life asset management activities. This will include:

o Population-based predictive analytics. Typically used for smaller value, non-critical pumps to support time-based maintenance strategies.

o Operations-based population predictive analytics. An extension of population-based analytics, but including the impact of operational environment and duty cycle into the analysis.

o Predictive models of performance degradation.

o Operational anomaly detection and normal behaviour benchmarking.

The position will be hosted in the Department of Electronic and Electrical Engineering is internationally recognised for its research excellence, industrial engagement and first-class teaching programmes. Further information on the Department can be found at https://www.strath.ac.uk/engineering/electronicelectricalengineering/. The successful candidate will join the Intelligent Systems Team in the Advanced Electrical Systems group in the Institute for Energy and Environment in the Department. While the primary focus will be the delivery of the project successful candidates will join a vibrant team delivering a wider range of projects across a range of industry partners, with opportunities for career development through and potentially beyond the fix-term of the post.