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Application of Grey Linear Exponential Model in Fault Prediction 灰色线性指数模型在故障预测中的应用
Pub Date : 2015-01-01 DOI: 10.3788/m0005220152209.0106
Huang Yin
Fault data of weapon systems are small-sample grey sequence,which often take on the wobbly characteristic. It is found by research that modeling of the grey wobbly sequence does not satisfy the condition of GM( 1,1) model. Therefore,it is proposed to use the dynamic exponent transformation for transforming the grey wobbly sequence into a monotonically increasing sequence with certain grey exponent law,and then to establish a GM( 1,1) model,which is called as grey linear power exponent function curve model( GIM( 1)). For GIM( 1),the unary linear regression modeling method is used for model parameter identification. The results prove that GIM( 1) model has good fitting and prediction accuracy for the grey wobbly sequence of the weapon system's fault sequence,which not only has the advantage of grey identification algorithm,but also can meet the identification requirement of general system.
武器系统的故障数据是小样本灰色序列,往往具有不稳定的特征。研究发现,灰色摆动序列的建模不满足GM(1,1)模型的条件。因此,提出利用动态指数变换将灰色振荡序列变换为具有一定灰色指数律的单调递增序列,建立GM(1,1)模型,称为灰色线性幂指数函数曲线模型(GIM(1))。对于GIM(1),采用一元线性回归建模方法进行模型参数辨识。结果表明,GIM(1)模型对武器系统故障序列的灰色摆动序列具有良好的拟合和预测精度,既具有灰色识别算法的优点,又能满足一般系统的识别要求。
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电光与控制
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