Gai, Xiaohua
(School of electronic and electrical, Nanyang Institute of Technology engineering, Nanyang, Henan 473004, China.)
,
Guo, Xuejun
(School of mathematics and statistics, Nanyang Institute of Technology engineering, Nanyang, Henan 473004, China.)
,
Liu, Dengdi
(Institute of Military Simulation, Air Force Command College, Beijng 100097, China.)
The basic idea of the accelerated life test is to use the characteristic life under high stress levels to extrapolate the characteristic life under normal stress levels. The traditional motor acceleration stress extrapolation technique uses the Arrhenius model, and some scholars use the gray GM (1,1...
The basic idea of the accelerated life test is to use the characteristic life under high stress levels to extrapolate the characteristic life under normal stress levels. The traditional motor acceleration stress extrapolation technique uses the Arrhenius model, and some scholars use the gray GM (1,1) model to make up some shortfalls of the Arrhenius model. In this paper, the GM (1,1) model is improved by a new gray generation method based on the idea of mining the law of gray numbers. The improved GM (1,1) model is not directly modeled by the original data, but by taking the logarithmic transformation of the original data and then accumulating the data, the GM (1,1) model is established and analyzed prediction. The measured data of the motor show that the improved model has better modeling precision and stronger adaptability than the Arrhenius model and the traditional GM (1,1) model.
The basic idea of the accelerated life test is to use the characteristic life under high stress levels to extrapolate the characteristic life under normal stress levels. The traditional motor acceleration stress extrapolation technique uses the Arrhenius model, and some scholars use the gray GM (1,1) model to make up some shortfalls of the Arrhenius model. In this paper, the GM (1,1) model is improved by a new gray generation method based on the idea of mining the law of gray numbers. The improved GM (1,1) model is not directly modeled by the original data, but by taking the logarithmic transformation of the original data and then accumulating the data, the GM (1,1) model is established and analyzed prediction. The measured data of the motor show that the improved model has better modeling precision and stronger adaptability than the Arrhenius model and the traditional GM (1,1) model.
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