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• ASSESSMENT OF THE LUBRICATION CONDITION OF ROLLING BEARINGS USING CLASSIFICATION ALGORITHMS

  P.G. Krinitsin, S.V. Chentsov

  2024-08-12

  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.