Adaptive multi-parameter prediction model based on grey theory

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.