滚动轴承性能保持可靠性预测

Performance Continuity Reliability Prediction of Rolling Bearings

为了计算滚动轴承性能的失效程度,提出滚动轴承性能保持可靠性的新概念,基于最大熵原理,建立性能保持可靠性评估模型。由滚动轴承运行性能最佳时期的性能数据,基于自助再抽样法获取大量样本数据,运用最大熵原理构建样本数据的概率密度函数,根据小概率事件原理得到性能随机变量的置信区间。根据泊松计数原理,获取性能抽样数据落在置信区间之外的频率,计算滚动轴承在未来时间的性能保持相对可靠度,预测滚动轴承保持最佳性能状态的失效程度。试验证明,该模型无需事先设定性能阈值,也无需样本概率密度函数的先验信息,且预测最佳运行性能状态失效程度的准确度高。

A new concept is proposed for performance continuity reliability of rolling bearings,and a model for evaluating performance continuity reliability is established based on maximum entropy principle to calculate failure degree of performance of rolling bearings. According to performance data during optimal operation performance time of rolling bearings,the sufficient sample data is obtained using bootstrap re- sampling method. The maximum entropy principle is applied to build probability density functions of sample data,and the confidence intervals of random variables of performance are obtained according to small probability event principle. According to Poisson counting principle,the frequency of performance sample data that outside the confidence interval is obtained. The performance continuity relative reliability of rolling bearings in the future is calculated,and the failure degree of rolling bearings maintaining optimal performance situation can be predicted. The experiments show that the model can be used without setting performance thresholds in advance and any prior information on probability density function of sample,and the accuracy is high for predicting failure degree of optimal operation performance situation.