Parameter comparison of Acoustic Emission signals for condition monitoring of low-speed bearings

Abstract

The moving components of low-speed machines which require condition monitoring are mainly gears and bearings. Among them, bearings are the most critical component to be monitored. Besides providing rotating motion, they are required to carry heavy load. This study investigates the application of Acoustic Emission (AE) technique for condition monitoring of low-speed bearings. Experimental tests involving a new and an inner race damaged bearing were performed using a fault simulator operating at speeds ranging from 1200 rpm to 30 rpm. Typical hit-based AE analysis, time-domain statistical parameters and frequency-domain with modified peak ratio were calculated and compared to gauge the capability of the technique for use in condition monitoring of low-speed bearings.

Keywords:

• Condition monitoring
• low speed bearing
• acoustic emission
• time domain analysis
• frequency domain analysis
• hit-based AE parameters
• High Frequency Resonance Technique (HFRT).

Additional information

Notes on contributors

Y -H Kim

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.

A C C Tan

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. Andy is expanding his research into machine diagnostics/prognostics and has published over 100 reviewed research papers. He is with the Queensland University of Technology and his academic interests include dynamics of mechanical systems, noise and vibrations, and mechanism design.

B S Yang

Prof Bo-Suk Yang is the director of the Intelligent Machine Condition Monitoring & Diagnostics Centre at the Puyong National University in Korea. He received his PhD degree in Mechanical Engineering from Kobe University, Japan, in 1985. In 1985-1996 he was an assistant, associate and full professor at National Fisheries University of Busan. He has been a professor in Mechanical Engineering Department at Pukyong National University since 1996. His main research fields cover machine dynamics and vibration engineering, intelligent optimum design, and condition monitoring and diagnostics in rotating machinery. He has published well over 100 research papers in the research areas of vibration analysis, intelligent optimum design and diagnosis of rotating machinery. He is listed in Who’s Who in the World and Who’s Who in Science and Engineering, among others.