.png){kind=icon}
返回
Real-time performance reliability prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/24.983393.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The purpose of this paper is to describe an approach to real-time reliability prediction, applicable to an individual product unit, operating under dynamic conditions. The concept of conditional reliability estimation is extended to real-time applications using time-series analysis techniques to bridge the gap between physical measurement and reliability prediction. The model is based on empirical measurements, self-generating, and applicable to on-line applications. This approach has been demonstrated to the prototype level. Physical performance is measured and forecast across time to estimate reliability. Time-series analysis is adapted to forecast performance. Exponential smoothing with a linear level and trend adaptation is applied. This procedure is computationally recursive and provides short-term, real-time performance forecasts which are linked directly to conditional reliability estimates. Failure clues must be present in the physical signals, and failure must be defined in terms of physical measures to accomplish this linkage. On-line, real-time applications of performance reliability prediction are useful in operation control as well as predictive maintenance.
Keyword:
performance reliability
reliability prediction
survival assessment
time series analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
2.8K
被引数:
8.5K
机构
暂无机构信息
引用论文
The Thromboxane Receptor Antagonist S18886 Attenuates Renal Oxidant Stress and Proteinuria in Diabetic Apolipoprotein E-Deficient Mice.png)
Diabetes
DiabetesIF0
PREAI
分享收藏Iduronic Acid-Containing Glycosaminoglycans on Target Cells Are Required for Efficient Respiratory Syncytial Virus InfectionVirology
OAAI
VirologyIF0
OAAI分享收藏 没有更多内容
没有更多内容