摘要
为制定长寿命、高可靠性机电产品的修程,需要评估其贮存期内的剩余寿命。首先实施步降应力加速贮存寿命试验,获取失效数据及其性能退化数据;接着采用失效数据和三步分析方法建立阿伦尼斯加速模型,并通过数据折算公式将高应力下的性能退化数据折算到正常应力下;最后改进路径分类与估计模型,提出核路径分类与估计(KPACE)模型,并对某型机电产品的贮存寿命进行预测。结果表明,在小样本情况下,相对于统计模型,采用KPACE模型可以得到更高的预测精度和更小的离散度。
In order to carry out the repair process of electromechanical products with characteristics of long life and high reliability, the remaining life of them in storage period needs to be evaluated. The step-down-stress accelerated life testing （SDS-ALT） is implemented to test electromechanical product to get failure and degradation data. A three-step analysis method is developed to establish Arrhenius accelerated model by using failure data. Then, degradation data at higher stress can be converted to those at normal stress by data conversion formula. A new individual-based prognostic algorithm, named the kernel path classification and estimation （KPACE） model, which is entirely based on the converted degradation data, has been developed to predict remaining storage life estimation of electronic product. Evaluated results of remaining storage life for a type electromechanical product show that KPACE model has higher prediction accuracy and smaller variance than that of the statistic model for small specimen condition.
作者
潘宇雄
周桂法
汪旭
PAN Yuxiong;ZHOU Guifa;WANG Xu(CRRC ZIC Research Institute of Electrical Technology ＆ Material Engineering,Zhuzhou,Hunan 412001,China)
出处
《控制与信息技术》
2018年第5期62-65,共4页
CONTROL AND INFORMATION TECHNOLOGY
基金
国家重点研发计划项目(2017YFB1200800)
关键词
贮存寿命预测
核路径分类与估计模型
退化数据
机电产品
storage life prediction
KPACE （kernel path classification and estimation） model
degradation data
electromechanicalproduct
