Earticle

초록

영어

A modeling method which can predict the shelf life of various types of spare parts in a relatively short time is put forward in this article. At present, it is difficult to solve the problem of mass modeling because the shelf life prediction models for different kinds of spare parts are of great diversification. In this paper, the best fitting nonlinear variables are selected by Gram-Schmidt regression method, and the detailed steps of automatic modeling process are given, which have advantages of strong robustness and are easy in programming. Especially, it can eliminate the influence of multicollinearity among alternative models effectively. By using natural rubber heating elongation data, an example is taken to demonstrate the process of automatic modeling. The nonlinear regression models selected by automatic modeling process are consistent in Dakin equation, and the predict values of natural rubber shelf life are included in the storage period given by manufacturing plant.

목차

Abstract
 1. Introduction
 2. Key Steps of Automatic Modeling Process
  2.1 Similar Linear Regression Model
  2.2 Gram-Schmidt Orthogonalization Method
  2.3 Gram-Schmidt Regression Method
 3. Automatic Modeling Process of Nonlinear Regression Model
 4. Case Study
 5. Conclusions
 References

키워드

저자

• Zhiwei Li [ Mechanical Engineering College, Xinhua District, Shijiazhuang, 050003, China ]
• Qi Gao [ Mechanical Engineering College, Xinhua District, Shijiazhuang, 050003, China ]
• Xinliang Jiang [ Mechanical Engineering College, Xinhua District, Shijiazhuang, 050003, China ]
• Shenyang Liu [ Mechanical Engineering College, Xinhua District, Shijiazhuang, 050003, China ]
• Tielu Gao [ Mechanical Engineering College, Xinhua District, Shijiazhuang, 050003, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605