Introduction
The Intelligent Systems Design research area focuses on the design, analysis, and engineering of software-intensive systems that interact with physical processes and operational data. A primary application domain is Cyber-Physical Systems (CPS), with particular emphasis on industrial CPS, where sensing, computation, communication, control, and physical behaviour form an integrated operational system.
Research areas include algorithm design for anomaly and fault detection and identification, data validation and sanitisation for industrial time-series data, Explainable AI (XAI), system modelling, and requirements-driven design. The work addresses both the development of intelligent-system solutions and the analysis of their reliability, behaviour, and supporting data.
Research Directions
Anomaly and fault detection and identification
Design and evaluation of algorithms for anomaly and fault detection and identification in industrial CPS, including machine-learning and time-series methods. Explainable AI (XAI) is used where appropriate to analyse model behaviour and support interpretation of detected system conditions.
Industrial CPS data quality
Methods and tooling for validating, characterising, and sanitising industrial time-series data before their use in analytics and machine-learning pipelines. This includes domain-specific approaches for expressing system, signal, and dataset expectations.
System modelling, requirements, and demonstrators
System-level modelling and analysis of embedded and cyber-physical systems, together with requirements-driven development of research demonstrators. This work connects system architecture and implementation choices with measurable operational and research objectives.
Selected Artefacts
Student Projects
Student work in this area includes CPS data pipelines, dependable ML, anomaly analysis, explainability, simulation, and intelligent-system design. See the Student Projects page for current opportunities and completed research.