Real-time Reliability Self-assessment in Milling Tools Operation

被引:5
|
作者
Liu, Shujie [1 ]
Hu, Yawei [1 ]
Liu, Chi [1 ]
Zhang, Hongchao [1 ,2 ]
机构
[1] Dalian Univ Technol, Dept Mech Engn, Dalian 116024, Peoples R China
[2] Texas Tech Univ, Dept Ind Engn, Lubbock, TX 79409 USA
关键词
reliability estimation; the milling cutters; state space model; particle filter; PARTICLE FILTERS; MAINTENANCE; PROGNOSTICS; SYSTEMS;
D O I
10.1002/qre.1932
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
To ensure reliable operations, online reliability assessment based on the system monitoring is essential, especially for the critical machineries or components with high safety requirements. The real-time reliability of the milling cutters in practice is one of the examples that decide the total manufacturing effectiveness and the quality of products. The research on how to best estimate cutters' reliability has gained popularity in recent years due to the need in prognostics and health management. The state space model (SSM), employed to recognize the underlying degradation state as a first order Markov chain, is widely used to model the residual life and reliability evaluation. In this paper, non-linear and non-Gaussian SSM are established based on the tool wear condition. The degrading tendency is predicted by the particle filter algorithm, and then the conditional reliability is calculated based on the degradation state and a pre-set threshold. The effectiveness of this approach was proven by a real case study provided. Copyright (c) 2015 John Wiley & Sons, Ltd.
引用
收藏
页码:2245 / 2252
页数:8
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