Method for predicting remaining service life of large rotating support on basis of small sample

A technology for slewing bearings and life prediction, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as undiscovered remaining life prediction methods for slewing bearings, achieve model accuracy reduction, speed up the test process, and save resources Effect

Active Publication Date: 2014-03-05
NANJING UNIV OF TECH +1
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Problems solved by technology

At present, no similar method for predicting the remaining life of slewing bearings has been found in China.

Method used

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  • Method for predicting remaining service life of large rotating support on basis of small sample
  • Method for predicting remaining service life of large rotating support on basis of small sample
  • Method for predicting remaining service life of large rotating support on basis of small sample

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Embodiment Construction

[0049] The present invention will be further described below in conjunction with accompanying drawing:

[0050] like figure 1 As shown, the implementation steps of this method are as follows:

[0051] 1) According to Weibull life distribution theory R=exp[-(t / η) β ], deduce the residual life prediction model of slewing bearing based on reliability R is the reliability, t is the number of turns of the slewing bearing, β and η are the slope of the Weibull distribution and the characteristic life of the corresponding bearing, which need to be determined through experiments.

[0052] 2) According to Hertz contact theory and ISO281 method, the load distribution of the slewing bearing raceway is solved. Suppose the external loads on the slewing bearing are: axial force F a , radial force F r and overturning moment M, for a certain ball, such as figure 2 As shown, let the contact force between the four points of contact with the raceway be Q 1x ,Q 1y ,Q 2x and Q 2y , sinc...

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Abstract

The invention discloses a method for predicting the remaining service life of a large rotating support. The method comprises the steps of deriving a remaining service life prediction model of the rotating support on the basis of the Weibull service life distribution theory, utilizing a test bed to exert a specific load on the rotating support, continuously running until the rotating support fails, dividing a roller path into 4n sections according to the rotating support roller path load distribution characteristics, measuring the volume abrasion loss of every section of rotating support roller path, and then utilizing the Archard abrasion theory and an inverse power law service life model to build a load-fatigue life distribution-remaining service life prediction model. According to the method for predicting the remaining service life of the large rotating support, remaining service life prediction of similar products can be achieved only by performing whole service life fatigue life testing on one rotating support, plenty of resources can be saved for enterprises, the product utilization rate is improved, and accident risks are reduced.

Description

Technical field: [0001] The invention relates to a method for predicting the life of a bearing, in particular to a method for predicting the remaining life of a large slewing bearing based on a small sample test. Background technique: [0002] Large-scale slewing bearings, as large-scale slewing connectors on fans and construction machinery, usually bear huge axial force, radial force and overturning moment. The failure and jamming of large slewing bearings usually leads to the shutdown of the equipment, which may have catastrophic consequences in serious cases. If the remaining service life can be accurately predicted, the operator can be guided to maintain or replace the slewing bearings in time, which avoids unnecessary Maintenance work can reduce the occurrence of major accidents. Therefore, it is necessary to accurately predict the remaining life of the slewing bearing. [0003] There are generally two types of widely used life prediction methods: data-driven methods ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 黄筱调封杨陈捷王华洪荣晶
Owner NANJING UNIV OF TECH
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