摘要
为解决性能退化轨迹建模中的小样本训练问题,研究了基于统计学习理论的支持向量机回归原理,提出了基于支持向量机回归模型的产品性能退化轨迹建模、寿命预测及可靠性评估方法。给出两种性能退化轨迹的支持向量机回归模型———单一模型和加权模型。实例分析表明,所提方法有较好的预测精度。加权支持向量机回归模型可在早期实现较高精度的寿命预测,提高性能退化的可靠性评估精度,从而可缩短试验时间,节约经费开支。
To solve the problem of few training samples in modeling the path of performance degradation, the regression principle of support vector machines （SVM） based on the statistic study theory is studied. Based on the support vector machine regression （SVR） model, the methods of modeling the degradation path, lifetime prediction and reliability assessment are presented. Two kinds of performance degradation path models, single SVR model and weighted SVR model, are proposed. The example analysis indicates that the precisions of the presented models are higher than the radial basics function neural network. Specially, the weighted SVR model can be used to predict lifetime in early time, thus shortening the test time and saving outlay.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第5期1246-1249,共4页
Systems Engineering and Electronics
基金
国家自然科学基金重点项目(60736026)
国家教育部新世纪优秀人才支持计划项目资助课题
关键词
可靠性评估
寿命预测
性能退化
支持向量机
reliability assessment
lifetime prediction
performance degradation
support vector machine
