Grease Contamination Detection in the Rolling Element Bearing Using Deep Learning Technique

Vibration Analysis is one of the most effective
methods used for the condition monitoring of rolling element
bearings. The early failure of bearing is mainly due to the
presence of solid particles in the grease lubricants. The
condition of lubrication in the bearing is an essential
parameter to meet the various demanding conditions of the
system. This paper aims to analyze the effect of lubricant
contamination by solid particles on the dynamic behavior of
rolling bearing and to classify them using a support vector
machine (SVM) and deep learning algorithm. Experimental
tests have been performed with 50 and 100 mg of sand dust
particles added to the ball bearings to contaminate the grease
lubricant at full load conditions. Vibration signals were
analyzed in terms of RMS, kurtosis, skewness, and peak to
peak for fault type classification using SVM. In deep learning,
the raw vibration signals are converted into a spectrogram
image and fed to convolution neural networks (CNN) for
fault classification. The results indicate that both SVM and
deep learning techniques are effective for fault classification
under the influence of lubricant contamination.