Condition monitoring of low-speed bearings — a review

Abstract

Machines with operating speeds less than 600 rpm are classified as low-speed machinery. These machines are usually the most critical items in the production line, generally large and have high rotating inertias. Until recently, there has not been much interest in condition monitoring of these machines as they do not fail easily. However, if an expected failure does occur, the downtime and replacement costs can be enormous, which can lead to massive production loss. Unlike those medium- to high-speed machines, condition monitoring of low-speed machinery presents a challenge as traditional velocity and acceleration sensors are only sensitive to responses with high impact rates. The moving components of these machines that require condition monitoring are mainly bearings and gears. Due to the lack of interest until recently, there is limited literature on condition monitoring of low-speed machinery. In this review, the authors have attempted to gather together the innovative and advanced approaches on condition monitoring of low-speed machinery, with the main focus on rolling element bearing condition monitoring.

Keywords:

• Condition monitoring
• low speed bearing
• vibration measurement
• Acoustic Emission (AE)
• ultrasound method
• statistical analysis
• bearings
• machine diagnostics
• maintenance

Additional information

Notes on contributors

Y H Kim

Prof Andy CC Tan received his BSc(Eng) and PhD degrees in Mechanical Engineering from the University of Westminster, London. His research interests include noise and vibration condition monitoring, and sensors for active vibration control. He has applied adaptive signal processing and the blind deconvolution algorithms to enhance the desired signals corrupted by noise for the detection of incipient faults. These algorithms, together with Acoustic Emission sensors, are currently being used in low-speed machinery condition monitoring. He is expanding his research into machine diagnostics/prognostics. Andy is a Professor of Mechanical Engineering in the Faculty of Built Environment and Engineering at the Queensland University of Technology, Brisbane, and his academic interests include dynamics of mechanical systems, noise and vibrations, and mechanism design. He is a Fellow of Engineers Australia.

A C C Tan

Yong-Han Kim received his BS, Masters and PhD degrees in Mechanical Engineering from Pukyong National University, Korea, in 1997, 1999 and 2004, respectively. His research background is in evolutionary algorithms and its application for optimum design and parameter identification of rotating machineries. Currently, he is a research fellow in the research centre for Engineering Asset Management, Queensland University of Technology, Brisbane. His primary research interests are in the areas of diagnostics and prognostics of rotating machineries, and the application of acoustic emission techniques for condition monitoring.

V Kosse

A/Prof Vladis Kosse has more than 30 years experience as an academic, researcher and consultant. He started his academic career in Ukraine at the Azov State technical University. He moved to Australia in 1992, working for Monash University, Melbourne. Since 1999, he has been at the Queensland University of Technology, Brisbane, being responsible for engineering design. Vladis’s area of research interest includes drive-train dynamics, computer modelling of transition processes in drives, machinery failure analysis and engineering creativity. He is internationally renowned as an expert on the Theory of Inventive Problem Solving (TRIZ). He has published more that 80 refereed papers and a book Solving problems with TRIZ — an exercise handbook, which has been printed in three international editions in the USA and Japan.