MODELING OF BEARING MAINTENANCE BASED ON NEURAL NETWORK AND MAINTENANCE WINDOW

ZHANG Xin; ZHAO JianMin; NI XiangLong; LI HaiPing

doi:10.16579/j.issn.1001.9669.2018.01.008

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MODELING OF BEARING MAINTENANCE BASED ON NEURAL NETWORK AND MAINTENANCE WINDOW

更新时间：2022-09-22

- MODELING OF BEARING MAINTENANCE BASED ON NEURAL NETWORK AND MAINTENANCE WINDOW
- Journal of Mechanical Strength Vol. 40, Issue 1, Pages: 45-49(2018)
- 作者机构：
  
  1. 军械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2018.01.008
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ZHANG Xin, ZHAO JianMin, NI XiangLong, et al. MODELING OF BEARING MAINTENANCE BASED ON NEURAL NETWORK AND MAINTENANCE WINDOW. [J]. 40(1):45-49(2018)
DOI：

ZHANG Xin, ZHAO JianMin, NI XiangLong, et al. MODELING OF BEARING MAINTENANCE BASED ON NEURAL NETWORK AND MAINTENANCE WINDOW. [J]. 40(1):45-49(2018) DOI： 10.16579/j.issn.1001.9669.2018.01.008.

摘要

运用前馈神经网络对轴承寿命分布进行预测,并提出了基于维修窗的维修决策模型。首先运用神经网络对轴承寿命分布的均值和方差进行预测,从而得到轴承的寿命分布,提出基于维修窗的维修决策模型,并与基于阈值限维修模型进行对比。并对不同的维修窗位置与最优维修点的关系进行分析。通过运用轴承全寿命退化数据对模型进行验证,计算出两种模型的维修费用率,得出在一定的时间区间采用基于维修窗的维修决策模型优于阈值限维修模型的结论。

Abstract

The feed forward neural network is used to predict the bearing life distribution in this paper,and puts forward the maintenance decision model based on the maintenance window. The mean and variance of the bearing life distribution are predicted by using neural network,and the life distribution of the bearing is obtained. A maintenance decision model based on the maintenance window is proposed and compared with the threshold value maintenance model. The relationship between different maintenance window position and optimal maintenance point is analyzed. The model is verified by using the data of the whole life of bearing,it is concluded that the maintenance decision model based on the maintenance window is better than the threshold limit maintenance window is better than the threshold limit maintenance model in a certain time interval by calculate the maintenance cost rate of the two models.

关键词

滚动轴承前馈神经网络预测维修窗

Keywords

Roller bearingFeed forward neural networkPredictionMaintenance window

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