A visual interpretation of data represented by coloured dots appearing at first scattered in space, then becoming organised into a structure

Innovative Industrial Research group

We research the use of machine learning/AI to solve the most pressing industry challenges of tomorrow.

Within the Innovative Industrial Research Group, we're working on improving performance through the integration of AI into products and services.

The research we're doing can be applied across many different industries, from dairy processing to aviation services. We've collaborated on research projects with national and international companies and organisations – including STS Defence Ltd, Flight Data Services (FDS) Ltd, L3 ASV Ltd and Primority Ltd.

We work with our clients to identify where machine learning can have the greatest positive impact, then focus on how we can help integrate new technology into a company's existing infrastructure and culture.

Our research has been funded by major bodies – such as Innovate UK, the Engineering and Physical Sciences Research Council, the Defence Science and Technology Laboratory, and the European Union (EU) – and our work is focused on overcoming the barriers that are preventing companies from adopting machine learning, including:

  • Data - storage is becoming cheaper all the time, but it can be unclear what types of data should be collected, and how frequently
  • Staff - there is a real shortage of staff trained in data engineering and data analysis
  • Algorithms - Algorithms need to be customised for a particular task, depending on data volume and labelling, which can vary dramatically from business to business

To overcome these obstacles, we've made increasing the intelligence of our algorithms a constant pursuit. By advancing semi-supervised learning, unsupervised learning and the development of a common learning architecture, we're making it easier for businesses to adopt machine learning, especially in Small to Medium Enterprise (SMEs), where such an investment would normally be more difficult to justify.


Our research covers the following topics


  • Predicting anomalies within milk filling machines at UK dairies
  • Detecting product defects on high speed production lines
  • Identifying anomalies within the supply chains of food companies to improve food safety


  • Predicting known and unknown anomalies on marine engines
  • Predicting anomalies on the powertrain and battery pack of autonomous surface vessels
  • Using machine learning to minimise the impact of rail delay
  • Accurately navigating autonomous boats in the absence of GPS

High-performance Computing

  • Optimising data centre server power consumption
  • Optimising data centre storage energy usage

Areas of expertise

The work we're doing in the Innovative Industrial Research Group maps to the following areas of research expertise.

Research projects

Find out more about our most recent research projects – including those that are currently underway and about to begin.

  • We're working with STS Defence Ltd, a Gosport based SME, and other partners, to develop IConIC (Intelligent Condition Monitoring with Integrated Communications). IConIC is a predictive monitoring tool for marine engines, being developed with a £1.4 million project funded by Innovate UK. This approach has been patented, and has been recently launched.
  • We're currently working with Primority Ltd and Food Standards Scotland on a project funded by Innovate UK, using machine learning to detect anomalies within food supply chains that could lead to food safety/allergen incidents, or expensive product recalls
  • We're working with the University of Sheffield and KCC Ltd, a supplier of eco-friendly food cartons, to explore how quality control can be automated on an EPSRC funded project
  • We're working with colleagues in the School of Maths and Physics as well as L3ASV Ltd, Houlder Ltd, SeaRoc Group Ltd, Offshore Catapult to research the feasibility of using autonomous surface vessels to deliver maintenance cargo to offshore windfarms, reducing wind farm downtime costs

  • We recently won a double Knowledge Transfer Partnership (KTP), to work with STS Defence in developing an intelligent communications unit, to select the appropriate communications bearer for onward data transmission based on cost, data urgency and bearer availability

  • We've worked with Flight Data Services (FDS) Ltd, a Whiteley based SME, in developing ways to improve the analysis of commercial aviation data. Through 3 Knowledge Transfer Partnerships (KTPs) and an EPSRC Industrial CASE studentship, we helped FDS create new products and services, as well as aided in the development of a data orientated culture.
  • We've worked with Stork Food and Dairy Systems to develop predictive monitoring for milk filling machines. Catastrophic breakdowns can cost upwards of £300,000 per day in lost production, repairs and breach of supermarket supply contracts. We helped develop The Virtual Engineer, which uses anomaly detection algorithms to detect the first initial signs of a fault, and alert the user so that it can be addressed during normal maintenance. It's estimated to have saved at least £2 million.


Past and present research is conducted in partnership with many different organisations, including:


  • Flight Data Services Ltd
  • STS Defence Ltd
  • Primority Ltd
  • Food Standards Scotland
  • KCC Ltd
  • L3 ASV Ltd
  • Houlder Ltd
  • SeaRoc Group Ltd
  • Offshore Catapult
  • Muller Wisemann Ltd
  • Moodys Ltd
  • Britpip Ltd
  • TPG Services Limited (Polaris)
  • First MTR South West Trains Limited
  • Satellite Applications Catapult
  • BMT Argoss
  • SoftIron Ltd
  • Hyperdrive Ltd
  • Xyratex Ltd (now owned by Seagate Technology Ltd)
  • Prosig Ltd


  • University of Sheffield
  • University of Edinburgh
  • University of Southampton
  • University of Nottingham

Our members

Image of Dr Edward Smart

Dr Edward Smart

  • Job Title Senior Research Fellow
  • Email Address cam71265@myport.ac.uk
  • Department School of Energy and Electronic Engineering
  • Faculty Faculty of Technology
  • PhD Supervisor PhD Supervisor
Image of Dr Hongjie Ma

Dr Hongjie Ma

  • Job Title Senior Research Fellow
  • Email Address hongjie.ma@port.ac.uk
  • Department School of Energy and Electronic Engineering
  • Faculty Faculty of Technology

This site uses cookies. Click here to view our cookie policy message.

Accept and close