It has been shown that Acoustic Emission (AE) can be used to classify different friction states and identify defects in journal bearings. In addition, it has been demonstrated in experimental setups that AE can be used to estimate the wear volume of sliding lubricated metallic contacts. The aim of our work is to monitor the wear during the operation of a journal bearing. This work deals with the development of a prognosis system for a detection of the wear and its volume by means of AE before an actual fault occurs. For this purpose, a journal bearing test bench was developed, where the oil temperature for the lubrication, load and rotational speed can be set. Experiments were carried out under different operating conditions and lasted up to 20 hours each. To establish a correlation between wear and AE, the wear volume is determined by comparing recorded roundness profiles of the inner race before and after each experiment at multiple positions. The method used is validated by measuring the roughness of the inner race surface, thus confirming the position of the wear. The analysis of the recorded AE signals shows an amplitude modulation as well as bursts. The frequency of the amplitude modulation correlates with the rotational speed even at higher velocities. Bursts in the AE signal are directly related to wear. Various features, e.g. root mean square value and properties of the measured bursts, of the AE signal and their relevance for the prediction of the wear are analyzed. A quantitative estimation of the volume proves to be challenging, while a qualitative estimation of occurrence and severity of wear can be reliably detected.
acoustic emission, classification, journal bearing, wear, random forest classifier
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