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.

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

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

• Demonstration of AI algorithms implementation

 

As a Research Fellow, you will engage as an independent researcher in individual and collaborative research, establishing a distinctive programme of research and disseminating results through regular publications in high impact journals, books

and conference proceedings. You will apply, as Principal Investigator and/or Co-Investigator, to appropriate external bodies for research funding and manage grants awarded. You will manage a research team (students and staff), providing direction, support and guidance and you will participate in and develop external networks to foster research collaborations, to inform the development of research objectives and to identify potential sources of funding. You will develop knowledge exchange activities by, for example, establishing research links with industry and influencing public policy and the professions and you will collaborate with colleagues to ensure that research advances inform departmental teaching effort, including contributing to relevant teaching programmes as appropriate. You will carry out Department/School, Faculty and/or University administrative and management functions, for example through membership of committees and engage in continuous professional development.

 

To be considered for the role, you will be educated to a minimum of PhD level in an appropriate discipline, or have significant relevant experience in addition to a relevant degree. You will have research interests consistent with the strategic direction of the Department/School, a body of published research in high quality publications demonstrating standards of excellence, and an ability to develop research proposals and to attract funding and research students, as appropriate to the discipline, including experience of contributing to grant applications. You will have an ability to plan and organise research programmes, to ensure successful completion and you will have experience of planning and organising workloads, including the ability to supervise and delegate work. You will have some experience of teaching at undergraduate and/or postgraduate levels, an ability to work within a team environment and to lead teams and excellent interpersonal and communication skills, with the ability to listen, engage and persuade, and to present complex information in an accessible way to a range of audiences.

 

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

• 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.

 

 

Possession and maintenance of security clearance (SC) is an essential requirement for this post. Further details can be

found here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels/sc-guidancepack-

for-applicants