Dr A R Mills
Senior Research Fellow

Photo of Mr A R MillsAddress:
Dr A R Mills MEng, CEng
Department of Automatic Control and Systems Engineering
University of Sheffield
S1 3JD
Tel: (+44) (0)114 222 5634
Fax: (+44) (0)114 222 5138
Email: a.r.mills @ sheffield.ac.uk
Room: B20, Amy Johnson Building


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. I have worked in industry on aerospace and automotive applications in the UK and Germany before taking my current post within the University Technology Centre (UTC) supported by Rolls-Royce.  The UTC is embedded within the Department of Automatic Control and Systems Engineering and houses industrially relevant research in control and health monitoring systems engineering.

In my current role as Research Programme Manager and Senior Research Fellow for the UTC, 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 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 am 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.

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.

PhD Project Supervision

The following PhD projects in the UTC are examples of postgraduate research projects closely associated to this work:

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

If you are interested in undertaking a PhD within the UTC please visit our PhD Programme website to find information about PhD projects, our research training and how to apply.


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

  • 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.



Tanner, G.F. & Mills, A.R., 2009. Asset Condition Monitoring, US20090326784

Jackson, K., & & Mills, A.R., 2013. Condition Monitoring of a System Containing a Feedback Controller, WO2013034420

Wall, D.S., Jones, M., & Mills, A.R., 2014. Autonomous Asset Health Investigation and Contingency Management, GB1406551.0

Journals & Conference papers

Fu, R., Harrison, R. F., King, S., & Mills, A. R., 2016. Lean burn combustion monitoring strategy based on data modelling. In 2016 IEEE Aerospace Conference.

Mokhtar, M., Edge, J. C. & Mills, A. R. Equipment Health Monitoring with Non-Parametric Statistics for Online Early Detection and Scoring of Degradation. in Annual Conference of the Prognostics and Health Management Society 2014.

Zaidan, M. A., Mills, A. R., Harrison, R. F. & Fleming, P. J. Gas turbine engine prognostics using Bayesian hierarchical models: A Variational approach. Mech. Syst. Signal Process. (2015). doi:10.1016/j.ymssp.2015.09.014

Zaidan, M. A., Relan, R., Mills, A. R. & Harrison, R. F. Prognostics of Gas Turbine Engine: An Integrated Approach. Expert Syst. Appl. 2, (2015).

Zaidan, M. A., Mills, A. R., Fleming, P. J., & Harrison, R. F. (2015). Bayesian Hierarchical Framework for Aerospace Gas Turbine Engine Prognostics. Expert Systems with Applications, 41(1), 539–553.

Mansor, M.M., Giagkiozis, I., Wall, D.S., Mills, A.R., Purshouse, R.C., & Fleming, P.J., 2014. Real-Time Improved Power Management for Autonomous Systems, Proc.19th IFAC World Congress

Laslett, O., Zaidan, M.A., Harrison, R.F., & Mills, A.R., 2014. Fusing an Ensemble of Diverse Prognostic Life Predictions. Proc. IEEE Aerospace Conference 2014

Zaidan, M.A., Mills, A.R., & Harrison, R.F. 2013. Bayesian Framework for Aerospace Gas Turbine Engine Prognostics, Proc. IEEE Aerospace 2013

Skaf, Z., Zaidan, M.A., Harrison, R.F., & Mills, A.R., 2013. Accommodating Repair Actions into Gas Turbine Prognostics, Proc. PHM Society 2013

Kudikala, R., Mills, A.R, Holt, J.E., Tanner, G.F., & Fleming, P.J., 2013. Aero Engine Health Management System Architecture Design Using Multi-Criteria Optimization. Proc. Fifteenth International Conference on Genetic and Evolutionary Computation

Eleffendi, M. A., Purshouse, R., & Mills, A. R., 2012. Gas Turbine Fuel Valve Diagnostics, Proc. IEEE Aerospace 2012

Mills, A.R. et al., 2010. Heterogeneous Hardware Technologies for Accelerating Complex Aerospace System Simulations. Proc. IEEE Aerospace Conference 2010

Mills, A.R., Tanner, G.F., & Fleming P.J., 2010. An integrated approach to equipment health management system design and development, Proc. IEEE Aerospace Conference, Big Sky

Mills, A.R., Blakey, S, Wilson, C., Fleming, P.J. & Thompson, H.A., 2009. Time-frequency domain incipient fault detection with application to an aviation fuel system, Proc. Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies

Gunetti, P., Mills, A.R. & Thompson, H.A., 2008. A Distributed Intelligent Agent architecture for Gas-Turbine Engine Fault Detection, Isolation and Mitigation. Proc. 46th AIAA Aerospace Sciences Meeting and Exhibit. Las Vegas.

Mills, A.R., Tanner, G.F. & Thompson, H.A., 2007. Towards Prognosis of Gas Turbine Accessory Faults Using High Frequency Analysis Techniques. Proc.  The Fourth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies