A New Reliability Prediction Model in Manufacturing Systems

Guodong Li · Shiro Masuda · Daisuke Yamaguchi

52 citations0/0 files storedIEEE Transactions on Reliability

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Grey System Theory Applications Reliability and Maintenance Optimization Software Reliability and Analysis Research

Abstract

Reliability prediction has been widely studied in many research fields to improve product and system reliability in manufacturing systems. Traditionally, to establish the prediction model, modelers would use all training data without preference. However, the prediction model based only on the most recent data may have better performance. In this paper, to realize an accurate prediction with the most recent data sets, we use the grey model to establish the reliability model. Then, the cubic spline function is integrated into the grey model to enhance the prediction capability of GM(1, 1), a single variable first order grey model. The newly generated model is defined as 3spGM(1, 1). To further improve the prediction accuracy, the particle swarm optimization (PSO) algorithm is applied to 3spGM(1, 1). We call the improved version P-3spGM(1, 1). Finally, we validated the effectiveness of the proposed model using failure data sets of electric product manufacturing systems.

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References

1989Introduction to Grey system theory 2005Grey Information: Theory and Practical Applications 2007A grey-based rough decision-making approach to supplier selection 1997The hybrid grey-based models for temperature prediction 2001High-precision forecast using grey models 2002A high precision global prediction approach based on local prediction approaches

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