Professor Andrew Starr

CEng FIMechE FRAeS MIAM MInstNDT MISCM MPWI

Professor of Maintenance Systems

Centre for Life-cycle Engineering and Management

Contact Professor Andrew Starr

Tel: +44 (0) 1234 758374
Email: a.starr@cranfield.ac.uk
Twitter: @@AndrewGStarr
LinkedIn
Blog: http://www.linkedin.com/groups/Throughlife-Engineering-Services-3852571?gid=3852571&trk=hb_side_g
ORCID
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ResearchGate

Areas of expertise

Aeronautical Systems
Engines, Powertrains & Alternative Fuels
Industrial Automation
Instrumentation, Sensors and Measurement Science
Mechatronics & Advanced Controls
Sensor Technologies
Systems Engineering
Throughlife Engineering Services
Vehicle Health Management

Background

Professor Starr is Head of the Centre for Life-cycle Engineering and Management (CLEM), a world-leading centre of excellence in maintenance and asset management for high value systems. CLEM works in partnership with industry in research and education.

Professor Starr read Mechanical Engineering at the University of Leeds, while sponsored by British Aerospace (Civil Aircraft), for which he was awarded first prize in the final year of his apprenticeship. He studied for his doctoral thesis in condition based maintenance for robotic production plant at the University of Manchester, sponsored by Ford and Wolfson Maintenance. He has held academic posts at the University of Huddersfield, the University of Manchester, and the University of Hertfordshire, as Head of the School of Aerospace, Automotive and Design Engineering.

His research work has been funded by EPSRC, the EU, and a wide range of industrial partners. He is a Chartered Engineer, a Fellow of the Royal Aeronautical Society, a Fellow of the Institution of Mechanical Engineers, a member of the British Institute of Non-Destructive Testing, a member of the Institute of Asset Management, a member of the International Society for Condition Monitoring, and a member of the Permanent Way Institute. He has published over 150 technical papers from a wide range of collaborative projects with industry, helping to solve real problems and to devise innovative products and services.

Research opportunities

Prof Starr supervises research students focusing on machine and structural damage detection; diagnostics and prognostics; autonomous inspection, detection and robotics; "big data" for system monitoring and control; process monitoring; novel sensing and signal processing; algorithms and artificial intelligence; applications in railway infrastructure and vehicles; aerospace machines especially gearboxes; manufacturing, and defence systems.

Current activities

Projects focus on industrial maintenance research, including autonomous systems, failure modes, vibration and wear debris, wireless sensing, automation, decision-making mathematics, and management processes

2017-2022, EPSRC Platform Grant EP/P019358/1, Through-life performance: From science to instrumentation, £1.18m, Starr et al (PI), with Rolls-Royce, Bombardier Transportation, Network Rail, HVM Catapult.

Autonomous inspection and repair for railway applications:

2020-2022, Network Rail, 22245/02/6559, System Integration (part of In2Smart 2 Grant number 881574 WP13 in Shift2Rail) - unmanned ground vehicle
2020-2021, Network Rail, ECM22352, Autonomous Track Geometry Repair (part of In2Track2 Grant number 826255 4.4.2.1 in Shift2Rail)
2020-2021, Network Rail, ECM21659, Autonomous ultrasonic testing, (part of In2Track2 Grant number 826255 4.4.2.2 in Shift2Rail) (PI Durazo)
2020-2021, Network Rail, Remote monitoring of rail defects (part of In2Track2 Grant number 826255 4.5.2.1 in Shift2Rail), (PI Durazo)
2020-2023, Network Rail, PhDs in location, navigation and scale; modelling and test.
2018, Network Rail, 13453/02/6559 Rail Robot Demonstrator and Cobots, part of Shift2Rail In2Smart WP10, Grant number 730569 CIs Tomiyama, Durazo Cardenas, Kirkwood
2018, Network Rail, 13451/02/6559 Simulations and V&V for Railway Inspection and Repair System (RIRS), part of Shift2Rail In2Smart WP10, Grant number 730569, CI Tomiyama
2018, Network Rail, 11032/02/6559 Non-contact system for detecting rail defects based on laser Doppler vibrometry and thermography, part of Shift2Rail In2Smart WP3, Grant number 730569 CI Durazo Cardenas
2017-2020, Unipart Rail, PhD Studentship in “Big data” for predictive maintenance

Railway track

2020-2021, SNCF, Whole system design of next generation Switches and Crossings (part of In2Track 2 Grant number 826255 in Shift2Rail)
2018, Network Rail, 3441804 Self-adjusting Switches and Crossings, £49,000, part of Shift2Rail In2Track WP2, Grant number 730841, CI Durazo Cardenas

Gearbox degradation: surface damage and root cracking

2016-2021 IGear: Intelligent Gearbox for Endurance Advanced Rotorcraft - Cleansky 2 738144 - focusing on algorithms for reliable helicopters (coordinator)

Linear actuators - modelling and experimental degradation, diagnostics and prognostics

Clients

Active Space Technologies
Avio Aero
BAE Systems
Barden Bearings
Bombardier
Network Rail
NNL
Procter & Gamble
QinetiQ
Rolls-Royce
Schaeffler
Schlumberger
Scisys
Sellafield Ltd
SNCF
Thales
UK Space Agency
Unipart Rail

Articles In Journals

Conference Papers

Wood A, Kirkwood L, Feng Z, Alhaydhal S, Alomran A, Bin Taleb R, Durazo-Cardenas I & Starr A (2021) Cost data visualisation. In: Advances in Manufacturing Technology XXXIV, Derby, 7-10 September 2021.
Rahman M, Liu H, Durazo Cardenas I, Starr A, Hall A & Anderson R (2021) Towards an autonomous RIRS: design, structure investigation and framework. In: 7th International Conference on Mechatronics and Robotics Engineering, 2021 (ICMRE), Budapest, Hungary, 3-5 February 2021.
Namoano B, Starr A, Emmanouilidis C & Ruiz Carcel C (2019) Online change detection techniques in time series: an overview. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, 17-20 June 2019.
Nsugbe E, Starr A, Foote P, Ruiz-Carcel C & Jennions I (2016) Size differentiation of a continuous stream of particles using acoustic emissions. In: 20th Innovative Manufacturing Engineering and Energy Conference (IManEE 2016), Kozani, Greece, 23-25 September 2016.
Durazo-Cardenas I, Amaraegbeni C & Starr A (2016) Fusion of railway network data streams for asset usage in intelligent maintenance systems. In: EuroMaintenance 2016, Athens, 30 May - 1 June 2016.
Bevilacqua M, Tsourdos A & Starr A (2016) Egomotion estimation for monocular camera visual odometer. In: 2016 IEEE International Instrumentation and Measurement Technology Conference, Taipei, 23-26 May 2016.
Durazo-Cardenas I, Starr A, Turner C, Kirkwood L, Bevilacqua M, Baguley P & Shehab E (2015) An Intelligent Maintenance System for the Rail: Integrating Condition, Planning and Cost. In: Railway Engineering 2015, Edinburgh, 30 June - 1 July 2015.
Turner CJ, Tiwari A, Starr A, Durazo-Cardenas I & Blacktop K (2015) An intelligent framework and prototype for autonomous maintenance planning in the rail industry. In: 1st international conference on vehicle technology and intelligent transport systems (VEHITS 2015), Lisbon, 20-22 May 2015.
Bevilacqua M, Tsourdos A, Starr A & Durazo-Cardenas I (2015) Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems. In: IEEE 6th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2015, Kuala Lumpur, 9-12 February 2015.
Rodrigues D, Erkoyuncu J, Starr A, Wilding S, Dibble A & Laity M (2015) Review of the modelling approaches for availability contracts in the military context. In: 7th Industrial Product-Service Systems Conference - PSS, industry transformation for sustainability and business, Ecole des Mines Saint-Etienne, France, 21-22 May 2015.
Ruiz-Carcel C & Starr A (2015) Development of a novel condition monitoring tool for linear actuators. In: 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, Oxford, 9-11 June 2015.
Badi N, Phang A, Edwards R, Chen Y, Rzeszucinski P, Sinha J, Mao K, Jones R, Curran A, Shepherd D, Starr A, Khan M, Allen B & Marshall M (2011) The EUCAMS gear partnership - a model of industry/academic collaboration. In: 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies Conference, 2011, Cardiff, 20-22 June 2011.
Starr A, Hannah P, Esteban J & Willetts R (2001) Data fusion as a model for advanced condition based maintenance. In: 2001 International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM '01), Manchester.
Moore TD & Starr A (2001) Development of an automated fluorescent dye penetrant inspection system. In: 14th International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2001), Manchester, 4 September 2001 - 6 September 2011.
Hannah P, Starr A & Bryanston-Cross P (2001) Condition monitoring and diagnostic engineering - A data fusion approach. In: 14th International Congress on Condition monitoring and diagnostic engineering management (COMADEM 2001), Manchester, 4 September 2001 - 6 September 2011.
Starr A, Esteban J, Hannah P, Willetts R & Bryanston-Cross P (2000) Strategies in data fusion for condition monitoring. In: Quality, Reliability and Maintenance 2000 (QRM 2000), Oxford, 30-31 March 2000.
Starr A, Desforges M & Esteban J (1999) Strategies in data fusion - A condition monitoring approach. In: 1999 International congress on condition monitoring and diagnostic engineering management (COMADEM '99), Sunderland.
Boyd A, Connolly C & Starr A (1997) Surface defect and texture identification in construction materials. In: 6th International Conference on Image Processing and Its Applications, Dublin, 14-17 July 1997.
Starr AG (1997) Structured approach to the selection of condition based maintenance. In: 5th International Conference on Factory 2000: The Technology Exploitation Process, 1997, Cambridge, 2-4 April 1997.
Starr A (1995) 3D machine vision metrology. In: 2nd International Conference and Exhibition on Laser Metrology and Machine Performance (LAMDAMAP 1995), Southampton, 11 July 1995.

Books

Gilabert E, Jantunen E, Emmanouilidis C, Starr A & Arnaiz A (2013) Optimizing E-maintenance through intelligent data processing systems. In: Engineering Asset Management 2011, London: Springer, p. 1-9.
Mekid S, Starr A, Pietruszkiewicz & R (2010) Intelligent Wireless Sensors. In: E-maintenance. Holmberg K, Jantunen E, Adgar A, Mascolo J, Arnaiz A, Mekid S (ed.), London: Springer, p. 83-123.
Starr A, Al-Najjar B, Holmberg K, Jantunen E, Bellew J & Albarbar A (2010) Maintenance today and future trends. In: E-maintenance. Holmberg K, Jantunen E, Adgar A, Mascolo J, Arnaiz A, Mekid S (ed.), London: Springer, p. 5-37, ed. 1.