• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science

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Failure rate prediction based on
grey multiple linear regression model

GUO Lijin1,HE Xishuo1,2,3,XU Xinxi2,SHI Meisheng2,WANG Jihu3   

  1. (1.School of Electrical Engineering and Automation,Tianjin Polytechnic University,Tianjin 300387;
    2.Institute of Medical Equipment,Academy of Military Medical Sciences,Tianjin 300161;
    3.Tianjin Key Laboratory of Advanced Electrical Engineering and Energy Technology,
    Tianjin Polytechnic University,Tianjin 300387,China)
  • Received:2017-03-27 Revised:2017-08-15 Online:2018-11-25 Published:2018-11-25

Abstract:

We propose a grey multiple linear regression model to predict the failure rate of the oxygen system during the using year of oxygen equipment. Firstly, we find out the GM (1,1) model of the failure rate of the oxygen system equipment. Secondly, we calculate the relationship model of the oxygen system failure rate, oxygen equipment failure rate and the years of equipment in use, and plug the GM (1,1) model of oxygen equipment failure rate into the relational model. Finally, we calculate undetermined parameters by using the least square method. We analyze the failure rate prediction of the oxygen system, and the results show that the grey multiple linear regression model is superior to both the individual GM model and linear regression model in terms of prediction accuracy of failure rate. Moreover, the historical data in use does not require a typical distribution. The prediction results of the model can provide a decisionmaking basis for oxygen system maintenance work.
 

Key words: GM (1,1) model, multiple linear regression model, grey multiple linear regression, failure rate