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The University of Southampton
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Professor Ling Wang PhD, MSc, BSc, CEng, FInstNDT, FHEA

Professor of Tribo-Sensing, Head of nCATS Group

Professor Ling Wang's photo

Professor Ling Wang is Professor of Tribo-Sensing and Head of nCATS Group within Engineering and Physical Sciences at the University of Southampton.

Combining tribology with sensing technologies provides intelligence and produces smarter engineering systems

Ling is Professor of Tribo-Sensing at the national Centre for Advanced Tribology at Southampton (nCATS) within Mechanical Engineering Department in Faculty of Engineering and Physical Sciences. She has over 20 years research experience in the field of tribology and sensing. She chairs the University Strategic Research Group (USRG) on Monitoring of Engineered and Natural Systems Using Sensors (MENSUS) and is Head of National Centre of Advanced Tribology Centre at Southampton (nCATS).

Ling has published over 100 peer reviewed papers and led a number of collaborate research projects sponsored by EU, UK Research Councils and a range of industrial partners including Rolls-Royce plc., GE Aviation, Vestas Wind Systems, Shell Global Solutions, Afton Chemical Corporation, John Crane and Schaeffler Group, with an over £8M research grant portfolio.

Career History

Ling obtained both her BSc and MSc in Chemistry from Nankai University in China, and her Diploma in Environmental Engineering from Portsmouth University and PhD in Systems Engineering from Southampton Solent University in the UK. She joined the Tribology and Surface Engineering Group at University of Southampton since 2001.

Research interests

  • Condition monitoring, sensor technologies and advanced signal processing techniques:
    • Bearing failure detection using vibration, acoustic emission and electrostatic sensing techniques.
    • Data mining and fault prediction using artificial intelligence and statistical techniques.
    • Thick-film sensor development for oil degradation monitoring.
  • Wind turbine bearing failure (white etching cracking) research: rolling contact fatigue, subsurface crack analysis and crack network 3D modelling; material analysis using SEM/XRD; FIB/TEM; hydrogen content analysis; white etching crack monitoring using multiple sensors.
  • Engine oil age detection using thick-film sensors (online oil acidity measurement).
  • Surface Engineering: friction modification using self-assembled monolayer, polymer brushes and surface texturing.
  • PI, 'A study on the influence of lubricant chemistry on formulation of white etching cracks under sliding/rolling conditions', sponsored by Zeller+Gmelin GmbH & Co. KG, 2019 – 2022.
  • Co-I, 'CO2-based electrosynthesis of Ethylene oxide (CO2EXIDE)', sponsored by EU SPIRE Programme (Grant agreement No. 768789), 2018 – 2020.
  • PI, 'Further understanding of rolling contact fatigue in rolling bearings', sponsored by Schaeffler Technologies AG & Co. KG, 2018 – 2021.
  • PI, ‘Integrated Intelligent Bearing Systems (I2BS) for UHPE Ground Demo’, sponsored by EU Clean Sky2 programme (Grant Agreement No.717174), 2016 – 2021.
  • PI, ‘White etching crack detection using multiple sensing and advanced signal processing techniques’, sponsored by Schaeffler Technologies and University of Southampton, 2016 – 2019.
  • PI, Royal Academy of Engineering Visiting Professors Award (VP1516/2/53) for Prof Walter Holweger at Schaeffler Technologies, 2015 – 2018.
  • Co-I, ‘Hybrid Medical Polymers (HyMedPoly)’, sponsored by EU H2020 (Grant Agreement No. 63050), 2015 – 2019.
  • PI, ‘Root cause of white etching cracks’, sponsored by Schaeffler Technologies AG & Co. KG, 2014 – 2018.
  • PI, ‘Develop green lubrication solutions for automotive engines’, sponsored by DSTL and EPSRC, 2014 – 2017.
  • PI, ‘Surface texturing for hydrodynamic bearings’, sponsored by John Crane and EPSRC, 2014 – 2017.
  • PI, ‘A study on the influence of lubricant and lubrication on white etching crack mechanism’, sponsored by Afton Chemical Corporation and EPSRC, 2013 – 2018.
  • PI, ‘Tribology and through-life maintenance strategies for premature wind turbine gearbox bearing failures’, sponsored by EPSRC EP/1033246/1, 2012.
  • PI, ‘Prediction of metallic materials wear behaviours from their chemical compositions using a backpropagation neural network approach’, sponsored by Rolls-Royce plc., 2012 – 2013.
  • PI, ‘Material Slip / Rolling Wear Tests’, sponsored by Rolls-Royce Nuclear, 2012 – 2013.
  • PI, ‘Thick film sensors for Engine oil acidity detection’, Shell Global Solutions (UK), 2010 – 2013.
  • PI, ‘White Structure Flaking (WSF) in Wind Turbine Gearbox Bearings: Effects of Butterflies and White Etching Cracks (WEC)’,  Vestas Wind Systems, 2009 – 2013.
  • PI, Travel grants from Royal Society and Royal Academy of Engineering, 2009.
  • 2018, won the British Institute for Non-Destructive Testing COMADIT Prize.

PhD supervision

  • Jürgen Wranik (2019 – )
  • Sotirios Mavrikis (2019 – )
  • Mostafa El Laithy (2018 – )
  • Ningxin Zhao (2017 – )
  • Kamran Esmaeili (2016 – )
  • Luis Vicente Pena (completed in 2019)
  • Viktorija Smelova (completed in 2018)
  • Simon Watson (completed in 2018)
  • Daniel Gropper (completed in 2018)
  • Alex Richardson (completed in 2018)
  • Ping Lu (completed in 2017)
  • Mostafa Soleimani (completed in 2014)
  • Robert Hanzal (completed in 2013)
  • Martin-Halfdan Evans (completed in 2013)
  • Suliang Chen (completed in 2010)
  • Jennifer Ang (completed in 2009)
  • Jun Sun (completed in 2007)

Research group

national Centre for Advanced Tribology at Southampton (nCATS)

Affiliate research group

Monitoring of Engineered and Natural Systems Using Sensors

Research project(s)

Assessment of complex electron beam textured rough surfaces

Development of automated condition monitoring using AI tools - Dormant

Health monitoring of new generation aircraft bearings

Structured Surfaces for Tribological Applications

Using structured surface rather than smooth surface to decrease friction is at variance with the classical tribology theories. However, it really happened and has been proved by worldwide researchers.

A study on White Etching Crack (WEC) root causes and its relation to material microstructures and surface treatments.

A study of Microstructure Alterations in White Structure Flaking Failures of Wind Turbine Bearings

  • Chair of MENSUS USRG since 2015
  • Head of nCATS since 2018
  • Admissions Tutor for Mechanical Engineering Courses between 2008 and 2012 (deputy in the first two years)
  • Senior tutor and coordinator for the MSc course on Advanced Surface Engineering and Tribology (2013 – 2015)
Sort via:TypeorYear




Module titleModule codeProgrammeRole
Engineering Principles GENG0005 Eng Phy & Geophy Fdn Yr - 3894 Lecture
Sensors and Signal Processing for Condition Monitoring SESG6035 MechEng - 3838; MSc (AMES) - 3882 Module Lead
Systems Reliability Prediction FEEG6006 MSc Unmanned Vehicle Sys Des - 3891 Lecturer

MPhil/PhD research

  • Modelling of textured tilting pad bearings
  • Polymer brushes for better lubrication of silicon nitride on steel hybrid contacts
  • Bearing failure investigation:
Dark etching region (DER)
Dark etching region (DER)
White etching band (WEB) (1)
White etching band (WEB) (1)
White etching band (WEB) (2)
White etching band (WEB) (2)
White etching band (WEB) (3)
White etching band (WEB) (3)
White etching crack (WEC) (1)
White etching crack (WEC) (1)
White etching crack (WEC) (2)
White etching crack (WEC) (2)
White etching crack (WEC) (3)
White etching crack (WEC) (3)
Butterfly in bearing steels
Butterfly in bearing steels
Rolling element bearing failure detection using a Gaussian Mixture Model based on multiple sensors
Rolling element bearing failure detection using Gaussian Mixture Model
Silicon nitride crack propagation detection using vibration and electrostatic sensing techniques
Silicon nitride crack propagation detection
Thick film sensors for oil age detection
Thick film sensors for oil age detection
Professor Ling Wang
National Centre for Advanced Tribology at Southampton (nCATS)
Faculty of Engineering and Physical Sciences
University of Southampton
Highfield, Southampton SO17 1BJ
United Kingdom

Room Number NNN: 7/4081/M7

Facsimile: (023) 8059 3016

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