【摘要】 故障预测对保障武器装备安全可靠工作具有重要意义。但是,用于武器装备故障诊断和预测的数据往往是小样本、多特征参数数据,当前主要的故障预测方法在实际故障预测中虽取得了一定的效果,但均存在不足之处。本文基于灰色预测建模理论,分析了GM(1,1)预测建模中的不足,考虑多个特征参数间的相互关系以及预测序列的实际特点,修正了初始值和背景值,建立了小样本情况下的自适应多特征参数预测模型,并以某型飞机发动机的多特征参数的仿真数据为例进行了预测分析,结果表明该模型具有很好的预测精度,证明了该模型的有效性。更多还原

【Abstract】 Fault prediction is of great importance to ensuring weapon equipment safety and reliability. Usually the data for fault detection and prediction of weapon equipment have features like small samples and multi-parameters. Currently although the main fault prediction methods have achieved certain success in practical application, they all fall short in some aspects. Based on the grey prediction theory and with an analysis of the disadvantages of GM(1,1)model, an adaptive prediction model with several characteristic parameters for small samples is put forward. This model modifies the initial value and background value, and takes into account the interrelations of the parameters and characteristics of prediction series. The model is then used for prediction and analysis with the multi-parameter data of an aero-engine. The results show that the model has good prediction precision, which in turn validates its availability.更多还原