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Real‐time Reliability Self‐assessment in Milling Tools Operation
Author(s) -
Liu Shujie,
Hu Yawei,
Liu Chi,
Zhang Hongchao
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1932
Subject(s) - prognostics , reliability (semiconductor) , reliability engineering , markov chain , particle filter , markov model , residual , computer science , condition based maintenance , engineering , quality (philosophy) , kalman filter , algorithm , artificial intelligence , machine learning , philosophy , epistemology , quantum mechanics , power (physics) , physics
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 © 2015 John Wiley & Sons, Ltd.