Qualifications & Experience
To commence in January/February 2021, this 4-year studentship is available for UK, EU and International* students, who possess a first class or 2.1 (Honours), or equivalent EU/International qualification, in the relevant discipline of Computer Science, Engineering, Physics or Mathematics. The candidate should have the following technical experience:
- Data analysis using Python programming language
- Familiarity of data analysis methods, including linear regression, machine learning/artificial intelligence, model test/training strategies and model optimisation
- The use of accelerated AI frameworks such as Tensorflow or similar
- Remote server-based data analysis that use Unix-like systems
- An inquisitive nature and desire to explore datasets thoroughly
- The ability to work with domain experts in developing data analysis solutions
The successful candidate will also have:
- A proactive approach, with initiative and ability to work independently
- Ability to: Synthesise, summarise and draw conclusions
- Adhere to strict standards of confidentiality
- Work in distributed teams
- Strength to cope with schedules and deadlines
- Excellent organisational and communication skills
- Excellent written and spoken English.
Find out more about this exciting PhD opportunity by clicking through the tabs above.
Individuals interested in this project should email firstname.lastname@example.org along with the title of the project you are applying for and attach your most up-to-date cv aligned with the requirements of this studentship.
We will only accept applications from international students who confirm in their email application that they are able to pay the difference between the Home/EU and International fees (approximately £16,500 per annum). The Stipend is not to be used to cover fees. If you are unable to cover this cost the application will be rejected.
Aim & Background
Introduction & Background
The NMIS (National Manufacturing Institute Scotland) Doctorate Centre in Advanced Manufacturing, along with Howden Compressors are looking to jointly fund a 4 year PhD studentship in the area of industrially applied data analysis for providing real-time insights into machinery condition.
Howden Compressors, in partnership with PTC and Microsoft have developed a platform to monitor the air and gas handling assets they design, test and manufacture for their clients all over the world. This Uptime platform has been commercialised and now have assets connected all over the world.
These assets range from small industrial fans to large multi-mega Watt bespoke compressor packages designed for hydrocarbon gas applications in potentially explosive environments. Now the assets have been connected and process and machine health data is being continually streamed into the platform from these assets we now have a historical dataset for a range of the fan and compressor technologies.
With this live and historical data when something is either going wrong or appears anomalous, or has went wrong, a Howden engineer can now look at the data and give guidance and advice to the end user either to solve the problem or what the root cause could be.
The student will work with experts at NMIS and Howden Compressors to develop algorithms that will provide improved prognostics and insights into asset quality, including expected time to component failure and evaluate the energy efficiency of certain systems. The algorithms will be developed using the labelled historical datasets and may use statistical methods, machine learning or artificial intelligence. Successfully developed models on historical data will then be transitioned to the Howden platform for further testing an evaluation as part of the wider decision support platform.
Research plan, current challenges & objectives
The aim of this project is to develop a set of tools and processes within the platform that can automate the detection of the anomalous behaviour across the entire spectrum of air and gas handling assets with respect to either the health or the performance of the asset. The majority of data is time-series but may also include frequency domain. The intention is to use the wide array of published models such as 1D CNN’s or alternatives and optimise them based on the data format and desired outcomes.
Specifically regarding the health of the assets the project should first conduct an audit of the serviceable components on the connected assets and then be able to measure and score the health / remaining life of the component, and track the model accuracy, by utilising the instrumentation on the package. This instantly provides benefit to the end users as they will then either be able to extend maintenance intervals or schedule them forward to prevent unplanned downtime. When the asset is provisioned with high frequency vibration monitoring instrumentation an automated reporting tool to score harmonic peaks and advise on potential failure modes is to be developed.
With regards to the performance monitoring on the assets that use huge amounts of electrical energy the objective here is to not only advise on the efficiency of the asset itself by looking at the energy consumed and the delivered flow versus the original design calculations but also asses control strategy of the assets on site.
By successfully completing this project the current Uptime platform will become more scalable, by removing a large portion of the manual reporting and automating it, than it currently is allowing the business itself to pivot more towards a repeatable aftermarket servitisation model. The models developed to support the Uptime platform will be demonstrated on a subset of systems within the Howden portfolio but will be designed to allow adoption within other systems after an appropriate model hyperparameter tuning and training period.
This fully-funded NMIS and industrial PhD opportunity will cover Home and EU Fees and Stipend.
We will only accept applications from international students who confirm in their email application that they are able to pay the difference between the Home and International fees (approximately £16,500 per annum). The Stipend is not to be used to cover fees. If you are unable to cover this cost the application will be rejected.
The University of Strathclyde supervisor for this PhD is Dr Hamilton.
Please note: We request that potential candidates do not contact Dr Hamilton and direct all questions to email@example.com.
Dr Hamilton is a Senior Research & Development Engineer in the National Manufacturing Institute Scotland (NMIS). His main research interests include industrially applied data analysis, development of data-driven autonomous decision support platforms, data operations (DataOps) in manufacturing environments and the Industry 4.0 technologies.
He joined the University in 2013 as a post-doctoral researcher within the Department of Electronic and Electrical Engineering (EEE) where he worked on developing sensor systems for aerospace manufacturing control and creating data-driven algorithms for the real-time prediction of the cure degree within composite components. Before becoming a Research Fellow, He worked on and led industrially orientated projects within the agriculture, pharmaceutical and oil & gas domains, primarily on the development of sensor systems and artificial intelligence models to support decisions.
How to apply
Individuals interested in this project should email firstname.lastname@example.org along with the title of the project you are applying for and attach your most up-to-date cv aligned with the requirements of this studentship. We will only accept applications from international students who confirm in their email application that they are able to pay the difference between the Home/EU and International fees (approximately £16,500 per annum). The Stipend is not to be used to cover fees. If you are unable to cover this cost the application will be rejected.