Analysis of Solid Contamination in Ball Bearing Through Acoustic Emission Signals

Abstract

Acoustic emission is one of the effective techniques used for the condition monitoring of rolling element bearings. Contamination is one of the major reasons for bearing early failure due to presence of solid particle in lubricant grease. In most cases, outer race is stationary and the inner race is attached to the rotating assembly. The lubrication is very essential for the bearing to perform under various demanding conditions. The main aim of this project is to analyze the effect of contamination of lubricant by solid particles on the dynamic behavior of rolling bearing. Green sand at three concentration levels 5%, 15%, 25% and different particle sizes 75 μm, 106 μm and 150μm is used to contaminate the lubricant. Experimental tests have been performed for different load and speed condition in good and contaminated ball bearings lubricated with grease. The trends in the amount of AE waves affected by the contamination of the grease were determined. Acoustic emission signals were analyzed in terms of RMS, kurtosis, and peak-peak.

Review of Tribological Failure Analysis and Lubrication Technology Research of Wind Power Bearings

“…Nevertheless, most of the measurements were measured during transient operation and are not up to standard for steady-state operation. Ibrahim et al [ 191 ] conducted experimental tests of grease in bearings with different concentrations and particle sizes of solid contaminants by using acoustic emission technique. Compared with the characteristic signals of bearings in normal operation, it is demonstrated through experiment that the root-mean-square and kurtosis values of the AE wave energy characteristics of the acoustic emission signal increase when there are larger particles in the lubricant.…”

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

“…Studies have shown that the size, weight and hardness of the particles are correlated to the amplitude of AE signals. 77,[83][84][85][86] Also, the number of particles seems to correlate to the hit-rate of the contamination initiated signals. 77,83 Besides these conventional characterisation approaches, machine learning algorithms -such as sparse dictionary learning 87 and a convolutional neural network 88 -have also been applied in early-stage studies to differentiate between contaminated and uncontaminated lubrication.…”