Tribology and Machine Condition Monitoring

In Tribology and Machine Condition Monitoring, we examine wear and fracture mechanisms, and study the wear properties of engineering and bio-engineering materials. These properties are examined using advanced techniques such as 3D imaging and quantitative wear analysis techniques at a micro- and nano-metre scale. We also use oil, wear debris and vibration analysis techniques for machine condition monitoring, and fault diagnosis and prognosis.

Tribology research group

Practical and innovative projects conducted in this area include:

    • Gear fault diagnosis using effective techniques
    • Integrated approach for diagnostics and prognostics of internal combustion engine bearings
    • Lubrication and contact fatigue analysis of gear teeth
    • Osteoarthritis assessment using wear debris analysis techniques
    • Wear simulation.

Tribology and Machine Condition Monitoring Laboratory:

    • Applied Mechanics Lab

Highlighted Projects

The Tribology and Machine Condition Monitoring group have two recent projects: 

  • Vibration-base health monitoring of aero-engine bearings (ARC Linkage project in collaboration with National Institute of Applied Sciences (INSA, Lyon) and Safran Group (France) as the industry partner.

This project will develop new vibration-based techniques to greatly improve the detection and diagnosis of faults in aero engine bearings from in-flight measurements. To achieve this goal, advances will be made on source separation algorithms to extract the weak bearing signals, and signal processing techniques to extract features for diagnosing bearing fault severity and lubrication conditions, under a wide range of operating conditions. A bearing degradation model will estimate the remaining useful life. Since rolling element bearings are among the most critical components in most machines, the results of this research will also provide massive benefits in other sectors such as mining, transportation, energy production and manufacturing.

  • Prognostics and Deep Learning for Propulsion System Health Management Phase (DSTG project)

This project is to develop an approach to modelling gear and bearing signals for anomaly detection and feature generation using long short-term memory based recurrent neural network.


Primary Academics

A/Prof. Zhongxiao Peng

Associate Professor Zhongxiao Peng

  • Tribology
  • Wear
  • Machine condition monitoring
Dr. Pietro Borghesani

Dr. Pietro Borghesani

  • Stochastic signal processing
  • Time frequency analysis
  • Cyclostationary analysis

Em. Prof. Bob Randall

Emeritus Professor Robert (Bob) Randall

  • Vibration
  • Machine condition monitoring

Dr. Md Rifat Shahriar

  • Machine condition monitoring
  • Signal processing techniques
Dr Wade Smith

Dr Wade Smith

  • Vibration
  • Machine condition monitoring

Associated Academics

Dr Kana Kanapathipillai

Dr Kana Kanapathipillai

  • Acoustics
  • Vibration
  • Condition monitoring
  • Mechanics of fracture and fatigue
Prof. Guan Heng Yeoh

Prof Guan Heng Yeoh

  • Numerical simulation of micro and nano-sized particles in artificial hip joints