Joint decision on replacement time and spare ordering time based on remaining useful life prediction

JIANG Yunpeng; GUO Tianxu; ZHOU Donghua

doi:10.11949/j.issn.0438-1157.20141517

您当前的位置：

首页 >

文章列表页 >

Joint decision on replacement time and spare ordering time based on remaining useful life prediction

更新时间：2025-12-10

- Joint decision on replacement time and spare ordering time based on remaining useful life prediction
- CIESC Journal Issue 1, Pages: 284-290(2015)
- 作者机构：
  
  1. 清华大学自动化系,北京,100084
  2. 中国船舶工业系统工程研究院,北京,100094
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20141517
  CLC： H17
- Online First：05 January 2015，
  
  Published：05 January 2015
- 稿件说明：
移动端阅览
JIANG Yunpeng, GUO Tianxu, ZHOU Donghua. Joint decision on replacement time and spare ordering time based on remaining useful life prediction[J]. CIESC Journal, 2015, (1): 284-290.
DOI：

JIANG Yunpeng, GUO Tianxu, ZHOU Donghua. Joint decision on replacement time and spare ordering time based on remaining useful life prediction[J]. CIESC Journal, 2015, (1): 284-290. DOI： 10.11949/j.issn.0438-1157.20141517.

摘要

针对带有状态监测的单部件系统

基于剩余使用寿命预测信息

考虑了替换时间和备件订购时间的联合决策。首先

利用实时状态监测信息对剩余使用寿命分布函数进行预测;然后

分别建立了该预测信息下的序贯优化模型和联合优化模型;最后

进行了实验研究

利用实际的轴承加速寿命实验对序贯决策和联合决策的效果进行比较。结果表明

联合决策比序贯决策要好

同时还避免了序贯决策中决策结果无法实施的问题。

Abstract

Based on the predicted information about remaining useful life

the joint decision on replacement time and spare ordering time for a single-unit system subject to condition monitoring is considered. First

the distribution function of the remaining useful life is predicted with real-time condition monitoring information; then

the sequential and joint optimization models are established based on the predicted information; finally

the effects of the joint decision and the sequential decision are compared by using the real bearing accelerated life test. The results illustrate that the joint decision is better

and it can avoid the problem under the sequential decision that decision results cannot be implemented.

关键词

Keywords

references

Views

330

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Overview of Digital Twin Systems for Fuel Cells

Optimization of heat storage and release cycle performance of Ca(OH)2/CaO indirect reactor

Exothermic characteristics of energy carrier for calcium looping thermo-chemical energy storage

Recent advances in machine learning for modeling and control of crystallization processes

Study on microstructure of NaCl solution ice centrifugal separation based on Micro-CT and deep learning

Related Author

YAO Xiaoduo

XU Qianghui

ZHANG Wenqiang

Related Institution

Yangtze Delta Region Academy, Beijing Institute of Technology

School of Mechanical Engineering, Beijing Institute of Technology

School of Mechatronical Engineering, Beijing Institute of Technology

⁰