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中文摘要：
产品的剩余寿命预测是其维修、更换和备件策略制定的重要依据。目前的寿命预测方法一般仅利用产品自身的性能退化数据,当性能退化数据较少时,剩余寿命预测结果精度难以保证。针对性能退化过程为具有随机效果的Wiener过程的产品,对其进行寿命预测时,采用Bayes方法融合产品的历史寿命信息和该产品自身的性能退化信息,得到性能退化参数的Bayes估计,进而得到该产品的剩余寿命分布,从而提高剩余寿命分布的预测精度。金属化膜脉冲电容器剩余寿命预测分析实例表明了该方法的有效性。
英文摘要：
Residual lifetime distribution prediction of products is of great importance in maintenance, replacement and spare parts decision making.The majority of existing methods use only the degradation data of products themselves,so precision of the prediction results is hardly satisfying when the degradation data are few.This paper tackles the problem using a Bayesian method.The degradation data of the products the Wiener process with random effect are modelled.Using the Bayesian method,featuring online degradation data and historical lifetime data are fused to derive the posterior distribution and Bayesian estimates of degradation parameters. Residual lifetime distribution is deduced,thus improving predictive precision.An example,residual lifetime distribution prediction of metallized film pulse capacitors,is presented to show the validity of the proposed method.
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