摘要
鉴于Gamma过程具有平稳、独立增量等退化建模所需的属性,将其用于描述设备退化过程,并针对缺乏故障数据时难以进行剩余寿命预测的问题,利用设备运行中采集的表征其退化状态的大量间接状态参数和少量直接状态参数,建立了基于Gamma退化过程的剩余寿命预测模型;针对经验最大化算法中似然函数难以解析求解的问题,引入粒子滤波算法实现了模型参数估计;最后将模型应用于直升机主减速器行星架的剩余寿命预测,得到了不同时刻的预测结果及95%置信区间,验证了预测模型的有效性和准确性。
Gamma process was adopted for independent increment property which is required items' degradation process description in consideration of its smooth and for degradation model. Moreover, for analysing the problem of remaining useful life prediction without sufficient failure data, a prediction model based on Gamma degradation process was established using the abundant indirect condition information and a few direct condition information that could reflect items' degradation state. Particle filters were introduced in experience maximization (EM) algorithm to obtain an analytical result of likelihood function. The prediction model was implemented on the planetary carrier of helicopter main gear-box and the remaining useful life prediction in 95 % confidence interval was achieved.
出处
《振动与冲击》
EI
CSCD
北大核心
2012年第14期47-51,共5页
Journal of Vibration and Shock
基金
总装重点预研基金装备健康状态评估理论与方法研究(9140A27020308JB34)
关键词
退化过程
剩余寿命预测
经验最大化算法
粒子滤波
Gamma过程
degradation process
remaining useful life prediction
experience maximization (EM)
particle fihering
Gamma process