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时变环境下的一种随机预测方法

A Stochastic Methodology for Prognostics Under Time-Varying Environmental Future Profiles
课程网址: http://videolectures.net/cidu2011_bian_stochastic/  
主讲教师: Linkan Bian
开课单位: 乔治亚理工学院
开课时间: 2012-06-27
课程语种: 英语
中文简介:
我们提出了一种基于传感器的退化信号的随机模型,用于预测实际时间内受到时变环境影响的各个组件的剩余寿命。我们考虑以确定性方式发展的未来环境特征。我们模型的独特之处在于历史数据与基于实时传感器的数据的结合,以更新贝叶斯框架内组件的降级模型和剩余寿命分布(RLD)。我们的模型的性能基于来自数值实验和使用真实轴承数据的案例研究。结果表明,与基准模型相比,我们的方法提供了更准确的RLD估计。
课程简介: We present a stochastic model of a sensor-based degradation signal for predicting, in real time, the residual lifetime of individual components subjected to a time-varying environment. We consider future environmental profi les that evolve in a deterministic manner. Unique to our model is the union of historical data with real time sensor-based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian framework. The performance of our model is evaluated based on degradation signals from both numerical experiments and a case study using real bearing data. The results show that our approach provides more accurate estimates of the RLD, compared with benchmark models.
关 键 词: 传感器的退化信号; 随机模型; 时变环境; 组件的剩余寿命; 历史数据; 实时传感器的数据
课程来源: 视频讲座网
最后编审: 2019-10-17:cwx
阅读次数: 97