Dr Zhongxiao Peng’s research focus has been wear analysis and machine condition monitoring. Her main research interests include 3D image acquisition, processing and quantitative analysis of wear in mechanical and bio-engineering systems; development and application of artificial intelligent techniques for prediction of the performance and remaining useful life of mechanical systems; integration of multiple techniques (wear analysis, vibration and acoustic emission) for machine health monitoring.
Field of Research (FoR)
Dr Zhongxiao Peng completed her PhD degree in Mechanical Engineering from the University of Western Australia in 2000. She joined James Cook University (JCU) as a lecturer in August 1999 and worked at JCU for 12 years. She led the Mechanical Engineering discipline over the period from 2008 to early 2011. Dr Peng joined the School of Mechanical and Manufacturing Engineering at UNSW Sydney in August 2011. She leads the Tribology and Machine...view more
Dr Zhongxiao Peng completed her PhD degree in Mechanical Engineering from the University of Western Australia in 2000. She joined James Cook University (JCU) as a lecturer in August 1999 and worked at JCU for 12 years. She led the Mechanical Engineering discipline over the period from 2008 to early 2011. Dr Peng joined the School of Mechanical and Manufacturing Engineering at UNSW Sydney in August 2011. She leads the Tribology and Machine Condition Monitoring research group at UNSW Sydney and works closely with EmProf. Robert (Bob) Randall, Dr Pietro Borghesani and Dr Wade Smith on a number of projects in the field of machine condition monitoring.
- Wear analysis of mechanical and bio-engineering systems
- 3D image acquisition, processing and quantitative characterisation of worn surfaces and wear debris at nano- and micro-scale
- Wear debris and vibration-based techniques for fault detection, diagnostics and prognostics of machinery
- Development and application of artificial intelligent techniques for simulating and analysing the degradation process of mechanical systems/components
Dr Peng and the Tribology and Machine Condition Monitoring group collaborate with many researchers within and outside Australia on a range of fundamental and application-orientated projects in the field of tribology and machine condition monitoring.
The Tribology and Machine Condition Monitoring group has a wide range of research facilities for wear testing, wear analysis and machine condition monitoring. They include two gearboxes, a rolling-sliding rig, a tribometer, high quality microscopes (optical and laser scanning microscopes), a number of quantitative image analysis packages, and extensive vibration instrumentation (including for acoustic emissions) and advanced signal processing packages developed in-house.
UNSW has many state-of-the-art image acquisition and examination facilities including laser scanning confocal microscopes, scanning electron microscopes, atomic force microscopes.
Awards and Service to the Profession
- 9 Australian Research Council (ARC) projects in 2000-2019
- Acting as an assessor for Australia Research Council and international research funding organisations
- A regular reviewer for more than 10 international journals
- Integrating wear debris and vibration analyses for the prediction of wear in spur gears (with EmProf. Robert Randall and Dr Pietro Borghesani)
- The estimation of gear surface roughness using vibration, acoustic emissions and wear analysis techniques (with EmProf. Robert Randall and Dr Wade Smith)
- Prognostics of rolling element bearings using experimental and simulation methods (with Dr Pietro Borghesani)
- Diagnostics in a planetary gearbox under variable speed conditions (with Dr Wide Smith)
- Numerical study of wear and wear debris generation
- Improvement of the wear resistance of bio-materials (e.g., UHMWPE, dental materials)
- Understanding and development of advanced meta-acoustic absorbing materials (with Professor Guan Yeoh)
Applying for a PhD position: Please email firstname.lastname@example.org with a copy of your CV and academic transcript(s).