Self-lubricating knuckle bearing accelerated degradation reliability evaluation and service life prediction method

Abstract

The invention provides a self-lubricating knuckle bearing accelerated degradation reliability evaluation and life prediction method. The method comprises the following preparation steps of: S1, acquiring accelerated performance degradation data of a knuckle bearing; S2, establishing a self-lubricating joint bearing accelerated degradation model based on a nonlinear Gamma process; S3, estimating unknown parameters of the self-lubricating joint bearing accelerated degradation model; and S4, performing self-lubricating joint bearing reliability evaluation and service life prediction. According to the method, the abrasion loss of the knuckle bearing is regarded as a performance parameter, the influence of various internal and external random factors on the performance degradation process of the bearing is considered, and the method has a non-stationary random time-varying characteristic, and is more in line with the actual condition of engineering, so that the evaluation precision is higher.

Description

technical field [0001] The invention belongs to an accelerated performance degradation test reliability analysis method, in particular to a self-lubricating joint bearing accelerated degradation reliability evaluation and life prediction method. Background technique [0002] Spherical joint bearings have the advantages of large bearing capacity, impact resistance and good lubrication, and are widely used in aviation, rail transportation, heavy equipment and other fields. At present, most spherical plain bearings have the characteristics of low friction, high performance, high reliability and long life, which brings great challenges to the traditional reliability analysis method based on failure time. Therefore, modeling based on performance degradation data of spherical plain bearings is a key way to solve its reliability analysis. However, due to the limitation of test time and test cost, the change of its performance parameters is extremely small within a limited time, an...

AI Technical Summary

Problems solved by technology

There are two existing reliability assessment methods for accelerated test of spherical plain bearings: one is based on accelerated life data, and then establishes a relationship model between stress and life; this method has an implicit assumption that the accelerated stress It is unrealistic in many cases to obtain life data at a certain level, especially in the case of tight development cycle and limited test time.

The other is a method based on accelerated performance degradation data. First, determine the failure threshold of spherical plain bearing performance degradation, and then establish a degradation model based on nonlinear regression analysis, extrapolate the bearing life, and then perform statistical analysis on the life data. However, this method ignores the The correlation between performance degradation data at different test moments sometimes leads to low evaluation accuracy

Images