摘要
针对目前发动机磨损状态监测中磨粒数量预测方法存在的问题,提出了基于灰色理论与时序模型相结合的预测方法,建立了灰色时序组合模型.通过灰色GM(1,1)模型模拟数据宏观变化趋势,并用时序AR(P)模型建立了残差序列以模拟数据微观变化趋势.通过对实测数据进行检验与比较,证明该组合模型在发动机状态监测中具有更好的预报效果.
Aiming at the problems of the wear condition monitoring,grey theory and auto-regressive combination forecasting model was put forward,and the combination model was build.The rough trend of the wear particle content change can be reflected through grey theory,and the detail of the change can be reflected through auto-regressive model.By testing and comparing a set of Ferro graphic data,the result shows that the combination model has a better forecasting result.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第8期246-248,共3页
Computer Engineering and Applications
基金
辽宁省教育厅高校科研项目(No.2006B031)
关键词
灰色理论
时序模型
状态监测
grey theory
auto-regressive model
condition monitoring
