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论文类型：期刊论文

发表时间：2016-11-01

发表刊物：QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

收录刊物：SCIE、EI、Scopus

卷号：32

期号：7

页面范围：2245-2252

ISSN号：0748-8017

关键字：reliability estimation; the milling cutters; state space model; particle filter

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.