Defect Diagnosis for Rolling Element Bearings Using Acoustic Emission

Abstract

Rolling element bearings are very common components in rotating machinery. Hence, condition monitoring and the detection of defects are very important for the normal and safe running of these machines. Vibration based techniques are well established for the condition monitoring of rolling element bearings, although they are not so effective in detecting incipient defects in the bearing. Acoustic emission (AE) is receiving increasing attention as a complementary method for condition monitoring of bearings as AE is very sensitive to incipient defects. This paper presents an experimental study to investigate the AE characteristics of bearing defect and validates the relationship between various AE parameters and the operational condition of rolling element bearings. To analyze the characteristic vibration frequency of the bearing using the AE signal, short-time rms and autocorrelation functions are integrated to extract the actual characteristic frequency. The AE signal is then analyzed using standard parameters of the signals to explore the source characteristics and sensitivity of typical rolling element bearing faults. The results demonstrate that the proposed method is very effective to extract the actual characteristic frequency of the bearing by AE signal. Furthermore the AE parameters are always sensitive to the running and fault conditions, which have a strong influence on the strain and deformation within the bearing material.

Analysis of Solid Contamination in Ball Bearing Through Acoustic Emission Signals

“…When the speed is increased to 900 rpm to 2100 rpm for 0 kg load KURTOSIS value of GB value is increased by 19.6% and for the particle size of 150 μm at 0 kg load the KURTOSIS value is increased by 21.3% for 5 kg load it is found to increase by 18.8%. From the above results the Kurtosis value first increases and then decreases, this is due to increase in speed of bearing the contamination thrown off due to acceleration [4,6,10].…”

“…The variation in defect size has a very little effect on the AE parameters. The slight change in speed affects the AE parameters greatly [6].…”

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

“…Other features like root mean square (RMS) energy, kurtosis and crest factor were used among others to detect bearing faults in [43].…”

Method for vibro-acoustic signal processing for detection of nascent defect of mechanical assemblies

“…The methods of detecting such defects were classified in [2,3,12,30]. A number of researchers have investigated the effect of acoustic emission in the detection of bearing defects [13][14][15][16]18]. In particular, it was noted that the seizing of gear and bearing pairs is one of the most common types of failure at high temperatures with a lack of lubrication.…”