Residual life prediction of rolling bearing under multiple stresses
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摘要: 为了实现多重应力下滚动轴承的剩余寿命预测,有效利用不用应力下的退化数据,提出了一种基于加速模型和贝叶斯(Bayesian)理论的滚动轴承剩余寿命预测方法。通过拟合优度检验和威布尔(Weibull)概率图检验法对滚动轴承试验中的数据进行有效性分析。利用switching Kalman filters(SKF)判断滚动轴承各时刻的退化状态。当滚动轴承进入加速退化时,用指数模型拟合轴承退化过程,利用广义线性对数模型表示退化模型参数与应力的关系,根据修正后的轴承实时退化数据利用贝叶斯算法更新模型参数,得到滚动轴承剩余寿命的概率密度函数,从而实现滚动轴承剩余寿命预测。采用XJTU-SY轴承数据集进行验证,预测结果的均方根误差在20 min以内,证明该方法能够有效预测滚动轴承的剩余寿命。Abstract: In order to realize the residual life prediction of rolling bearings under multiple stresses,a method for residual life prediction of rolling bearings based on acceleration model and Bayesian theory was proposed by effectively utilizing the degradation data under no stresses.The validity of the rolling bearing test data was analyzed by goodness of fit test and Weibull probability graph test.Switching Kalman filters (SKF) were used to determine the degradation state of the rolling bearing at each moment.When rolling bearing entered into the accelerated degradation,degradation process of bearing was fitted with exponential model,and the generalized linear logarithmic model was used to represent the relationship between degradation model parameters and stress;according to the revised bearing real-time degradation data,the model parameters were updated using Bayesian algorithm,the probability density function of the residual life of rolling bearing were acquired for residual life prediction of the rolling bearing.The XJTU-SY bearing data set was used for verification,and the root mean square error of the predicted results was less than 20 min,proving that the proposed method can effectively predict the residual life of rolling bearings.
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Key words:
- rolling bearing /
- multiple stress /
- acceleration model /
- residual life prediction /
- Bayesian
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