Artificial neural network multi-variable forecasting model of aero-engine complex wear trend

Multi-variable predicting model by back-propagation neural networks (BPNN) was established, and it considered the complex characteristics of the oil analysis data fully such as unequal interval sampling and affected by adding oil factor. In addition, the effect of ANN's parameters on the predicting accuracy was also discussed, and genetic algorithm (GA) was used to optimize ANN's parameters. Finally, two time series and aero-engine spectrometric oil analysis data were used to verify this model. The results show that this model can solve effectively the prediction problem of oil analysis data, which is affected by multi-factors such as unequal interval sampling and adding oil factor. The new method has important engineering application value, and it is a common method of forecasting complex time series, which is affected by multi-factors.