基于灰色马尔可夫模型的齿轮寿命预测

灰色GM(1,1)模型不适合描述随机波动性较大的预测问题,单纯应用该模型进行齿轮寿命预测时,预测精度不能保证;引入马尔可夫理论,建立了灰色马尔可夫预测模型,采用统计的方法,通过计算系统到达目标时刻的不同步数的转移概率矩阵,确定系统的目标状态。实例证明,相比与GM(1,1)模型,灰色马尔可夫模型的预测精度更高。

Prediction of Gear Life Based on Grey-Markov Model

Because of the problem that the grey GM(1,1) model is not suitable for the prediction of random and big fluctuant characters,the precision of gear life prediction solely with the grey GM(1,1) model is not to be guaranteed.The paper presents the Grey-Markov model to optimize the prediction based on the Markov theory.This model confirms the target state of the system by using the statistical method and calculating the diversion probability matrix of the system going to the destination within different steps.Finally a practical case in gear life prediction shows that the Grey-Markov model has a better prediction precision than grey GM(1,1) model.