A Bearing Life Prediction Method of Improving Smooth Degree and the Background Value

Yichi Zhang,Tao Shu,Yan Xu,M Anpo,F Song

doi:10.1051/e3sconf/202123602006

Abstract

Bearings, as a component in many complex weapons, can be used to reduce friction to improve the efficiency of equipment. Bearing CV value can quantify the working performance of bearings, which can act as a reference standard for staff to evaluate the working condition of bearings. According to the known data, the real CV value of the bearing is calculated in this paper. In order to improve the smoothing ratio, the data is processed by the idea of data transformation and the background value is optimized by the new formula. The two improve the GM (1,1) model and simulate the predicted bearing CV and calculate the moment of failure by this model, which is compared with the traditional GM (1,1) and the improved GM (1,1) by cumulative method in terms of error and accuracy. It is verified that the average relative error and the model prediction accuracy of the model prediction life are 0.0185 and 98.15% respectively after the improvement of the stability and background value. Therefore, this method has certain practical value in engineering, and is more effective than the cumulative GM (1,1) model.

Highlights

Bearings are present in most weaponry, for example, the trunnion section of the launcher arm of a missile launcher requires bearings to rotate, and many small and medium sized motors use bearings [1]
It is mentioned that the defects of the inner and outer rings will make the metal in contact surface flake off and form pits on the bearing surface, and the impact of the rolling body on the racetrack on one or both sides will cause various pollutants such as external viscosity and particles to enter between the rolling body and the racetrack, leading to wear or scratch, etc.[3].Using the maximum likelihood estimation method, the CV values of the bearings are calculated to quantify their operating conditions, and an algorithm is designed based on the original GM (1,1) method to predict the CV values by reducing the smoothing ratio of the original data series and optimizing the background value calculation method
In terms of relative error and prediction accuracy, this paper compares the former with the improved GM (1,1) model based on the cumulative method

Summary

Introduction

Bearings are present in most weaponry, for example, the trunnion section of the launcher arm of a missile launcher requires bearings to rotate, and many small and medium sized motors use bearings [1]. It is mentioned that the defects of the inner and outer rings will make the metal in contact surface flake off and form pits on the bearing surface, and the impact of the rolling body on the racetrack on one or both sides will cause various pollutants such as external viscosity and particles to enter between the rolling body and the racetrack, leading to wear or scratch, etc.[3].Using the maximum likelihood estimation method, the CV values of the bearings are calculated to quantify their operating conditions, and an algorithm is designed based on the original GM (1,1) method to predict the CV values by reducing the smoothing ratio of the original data series and optimizing the background value calculation method. The CV is calculated for the entire operating cycle of the rolling bearing, which has 984 moments of sampling for this bearing type, with a 10-min interval between each of the sampling moments, and the rolling bearing CV is calculated according to the method of maximum likelihood estimation

Calculation method of bearing’s real confidential value

Prediction method of bearing’s real confidential value

Improvement of smoothness

Background value optimization

Comparison of simulation results of three prediction models