基于ARIMA和RBF网络组合预测的惯性器件故障预报

Fault prediction of inertial component based on combined forecasting of ARIMA and RBF neural network

提出了一种基于ARIMA和RBF网络进行组合预测的方法,该方法综合运用ARIMA良好的线性拟合能力和RBF网络强大的非线性映射功能,将两种预测模型有机地组合在一起,综合各自优点,以期有效改善模型的拟合能力,获得最优预测效果。论文将该方法应用于某飞行器惯性器件的故障预报当中并进行了仿真实验。结果表明,这种方法相对于单项模型的预测具有更高的精度,对于复杂时间序列的分析和预测有很好的应用价值,在故障预报中具有广泛的应用前景。

A new method of combined predicting based on ARIMA forecast and RBF network is put forward in this paper. The ARIMA forecasting model has perfect linear fitting capability, and artificial neural network possesses the characteristics of strong nonlinear fitting. The two methods are combined together in order to integrate their own advantages, improve the fitting capability of the model and obtain the optimum predicting effect. The prediction performances of the method are tested by simulated experiment for a certain type of inertial component. The result shows that the combined model has better accuracy and it is an effective method for the fault prediction of inertialcomponent.