Programme Focus

The Cyber-Physical Systems programme aims to develop cutting edge capability to maintain and protect equipment, assets and infrastructures of marine industry through the convergence of operational and information technologies by seamless integration of physical components with modern computation, control and automation algorithms.

The programme uses advanced modelling, simulation and optimisation techniques based on AI, statistical and fuzzy logic approaches to discover salient and emerging properties of engineering systems and their operational characteristics. This is achieved through the application and effective usage of data-driven models and analytics, including dealing with big data and real-time data streams.

The Cyber-Physical Systems research programme is led by Dr Andrei Petrovski.

Overcoming Global Challenges

  • Smart sensors and inferential measurements: wind and wave turbines, pipelines and ROVs/UAVs
  • Hybrid digital twins: integration of virtual and physical system
  • Monitoring and diagnosis of industrial assets
  • Fault detection / identification / prevention
  • Industrial asset integrity management
  • Maritime Security Operations Centres: preventive maintenance and predictive automation
  • Specialised cyber ranges for protecting marine infrastructures

Current Research Projects

Enhancing Remote Condition Monitoring of Offshore Wind Turbines Using Machine Learning

Downtime for offshore wind turbines is very costly as it involves both the lost electricity generation and the costs of bringing crews to the turbines. Currently, there are considerable challenges with reliable failure prediction and condition-based maintenance programmes for generators.

The aim of the project is to create a specialised system using machine learning (ML) and Smart Data technologies that will monitor turbines operational condition, identify performance issues and predict future breakdowns. This will be achieved by collecting data from the wind turbines’ Supervisory Control and Data Acquisition (SCADA) system such as electrical, temperature and pressure, combined with vibration and acoustic data which will be obtained via sensors. A suitable source of existing SCADA data was identified – the Levenmouth Demonstration Turbine.  

This project focuses on carrying out an initial feasibility study on the use of wind turbine SCADA data to predict unhealthy conditions (alarms) in the generator. This will be used as a steppingstone in developing a cutting-edge condition monitoring system for wind turbine generators that reduce lost electricity generation due to malfunction, as well as reducing the need for unnecessary manual maintenance checks. The predicted impact of the project is that enhanced wind turbines should lead to a significant financial saving for windfarm operators, therefore further increasing business interest in the renewables sector.  

Inferential Measurement Systems for Enhanced Situation Awareness

Inferential measurement systems aim to model the relationship between primary characteristics that are difficult to measure directly and secondary variables that can be more easily monitored, either using on-board sensors or can be obtained from the ground stations. The situation awareness of unmanned aerial systems (UAS) can be considered as a primary characteristic affected by numerous variables ranging from the levels of perception (e.g., visibility of target and objects) to operational conditions (e.g., torque of the engine). The information obtained through inferential measurements can be used to control and optimise the operation of UAS without the need to install additional sensors on board or to excessively rely on the instrumentation data coming from the ground stations.

Various modelling paradigms may be used for inferential measurement, including data-driven modelling methods, such as time-series analysis, artificial neural network, Bayesian probabilistic modelling, support vector machines and genetic programming. The proposed approach for this project is to choose, build and evaluate an inferential measurement system for a UAS that is capable of enhancing its situational awareness through real-time analysis of sensor inputs and evaluating relevant secondary variables.

Programme Aims

  • Enable and accelerate digitally enabled transformation in the maritime environment
  • Integrate virtual and physical systems for monitoring, control and maintenance
  • Advance predictive automation technology to ensure asset integrity
  • Protect industrial assets against external and internal threats
  • Enhance operational resilience and sustainability of industrial assets
  • Develop cyber warning systems for education and training
  • Evaluate environmental risks and impacts of operational technologies


Programme Impact

  • Technical – Advance data analytical techniques to address industry and societal challenges
  • Environmental - Reduce hazards, regulate impacts and assess risks to the environment
  • Economic - Develop affordable and efficient technological solutions to the energy and maritime industries
  • Health and well-being – Protect the workforce dealing with operational technologies
  • Talent import - Create several professional and technical posts to attract worldwide talent