ASSESSMENT OF THE LUBRICATION CONDITION OF ROLLING BEARINGS USING CLASSIFICATION ALGORITHMS

Abstract

The purpose of this work is to solve the problem of unscheduled failures of rolling bearings installed on industrial equipment as a result of their improper maintenance during operation. It is known that up to 50% of all unscheduled downtime of industrial equipment occurs due to bearing failure. In this case, the main reason for bearing failures is violations of the lubrication regime of the rolling elements: excessive and insufficient quantities of lubricants. These reasons account for up to 36% of the total number of bearing failures. During equipment operation, it is very difficult to identify and prevent all problems with bearing lubrication, due to the wide variety of factors influencing their occurrence. Therefore, an urgent task for research is the development of an automated recommendation system for managing the maintenance of industrial equipment, with control of the lubrication of bearing units. The paper discusses a method for classifying the states of bearings depending on their diagnostic parameters: indicators of vibration velocity, vibration acceleration and temperature. For this purpose, classical machine learning algorithms are used: KNN, RandomForestClassifier and SVM models. For each model, hyperparameters are determined to achieve maximum results during training. In the process of conducting the study, an analysis of the influence of each of the diagnostic parameters - signs on the performance of the classification model was carried out. Understanding which indicator of bearing performance will be the most important will allow you to choose equipment condition monitoring devices at a manufacturing enterprise consciously, to solve specific production problems. The developed algorithm allows us to qualitatively, with 98% accuracy, assess the lubrication condition of rolling bearings and issue recommendations for timely maintenance of equipment. The classifier model is planned to be used as part of a complex for monitoring the technical condition of equipment, expanding diagnostic capabilities: in addition to information about the probability of equipment failure and predicted service life, the diagnostic complex, combined with the proposed model, will allow influencing the mileage of bearings by improving the quality of their lubrication.