Dr Andrew Mills

MEng, CEng

Department of Automatic Control and Systems Engineering

Senior Industrial Research Fellow

Research Programme Manager

+44 114 222 5634

Full contact details

Dr Andrew Mills
Department of Automatic Control and Systems Engineering
Amy Johnson Building
Portobello Street
S1 3JD

I graduated from The University of Sheffield in Control Engineering with sponsorship from the UK Government’s Engineering and Science Group and the Royal Academy of Engineering. After working in industry on aerospace and automotive applications in the UK and Germany, I took my current post within the University Technology Centre (UTC), supported by Rolls-Royce. Embedded within the Department of Automatic Control and Systems Engineering, the UTC houses industrially relevant research in control and health monitoring systems engineering.

In my current roles, I shape and co-ordinate a portfolio of industry and research council funded research, PhD projects and MSc projects aimed at delivering high quality academic and industrially exploitable research output. The projects are designed to maintain the UTC's position as Rolls-Royce’s first choice for control and monitoring systems research, and to develop the Research Centre’s expertise to be positioned for industries’ future control system needs.

Research interests

I'm investigating a broad range of topics related to Health Management Technologies, with application to equipment health management (EHM) system design, prognostic and diagnostic algorithms, and integration of monitoring with control function for through-life adaptable machines.

The research undertaken exposes the benefit of advanced Health Monitoring through in-house development, demonstration and analysis of new technologies. Technologies explored include advances in algorithms, sensors, and the acquisition, transfer, and management of monitoring data.

I have worked on developing and applying techniques directed at:- modelling and forecasting degradation with Bayesian models of uncertainty; modelling faults from data and physics; performance analysis with multivariate data analysis; design of diagnostic algorithms with time-frequency signal analysis and model-based techniques; model-based design of monitoring algorithms with deployment on FPGAs, DSPs and multi-core processors; providing intelligence for adaptation to autonomous systems.

I work closely with industry on integrated project teams, demonstration engine programmes and research programmes to ensure the insertion of technology into tomorrow’s products.

Research group

Martha Bin Zaidan: Bayesian forecasting of the remaining useful life civil aerospace gas turbine engines
Rajesh Kudikala: Optimisation of future gas turbine engine control system architectures
Aldo Villanueva Marcocchio: Smart wireless sensing for aero-engines
Zhou Sun: Novelty detection and compressive sensing
Ariel Cano: Advanced control strategies for managing dynamic integrated power systems
Romain Guicherd: Distributed model-based control for gas turbine systems

  • ASUR “Autonomous Multi-criteria Decision Making”, 2015.
  • TSB HITEA II, “Advanced Intelligent EHM” (PI), 2014–2017.
  • Rolls-Royce, “Control & Systems UTC” (CI), 2013–2015.
  • QinetiQ, “Applied Prognostics” (PI), 2013-2015.
  • EPSRC, “Through-life Service Feasibility grant” (PI), 2013–2014.
  • Rolls-Royce, “Wireless Sensing for Monitoring” (PI), 2013–2015.
  • TSB, “SILOET II WP.1.6 – Holistic Control & Health Monitoring” (CI), 2013–2015.
  • Rolls-Royce, “Wireless Sensing in Extreme Marine Environment” (CI), 2013–2013.
  • Rolls-Royce, “Autonomous Intelligence for Civil UAS” (PI), February 2012–February 2013.
Professional activities and memberships
  • Member IMechE / IET / RAeS Joint Propulsion Committee
  • Invited Panellist for Prognostics and Health Management conferences
  • Reviewer, various Aerospace conferences and Journals
  • Lecturer for Industrial Gas Turbine Controls Course in:
    • Gas Turbine Control Laws
    • Gas Turbine Software Architecture
    • Fault Tolerance and Control
    • Health Care Monitoring
    • System Identification.