To improve the aero-generator life prediction accuracy, an optimized auto-regressive and moving average (ARMA) model based on the genetic algorithm(GA) is presented. A specific experimental platform is used to perform long-term life prediction experiments on a certain type of aero-generator and collect the related test data. After a thorough analysis of these test data, a corresponding ARMA model is designed, and the genetic algorithm is used to carry on the exponent number optimization of the model. Then the original and the optimized ARMA models are used respectively to conduct life prediction research on the service life of the aero-generator. The result shows that these two models can realize the function of predicting the service life of an aero-generator. The average relative prognostic error of the ARMA model after the optimization is 2.26%, which is less than 4.33%, the error of the original model without optimization. Thus a conclusion can be drawn that the optimized ARMA model can predict the service life of an aero-generator more accurately and this model may find wide application in engineering practice.
CUI Jianguo, ZHAO Yunlong, DONG Shiliang, ZHANG Hongmei, CHEN Xicheng
. Life Prognostics for Aero-generator Based on Genetic Algorithm and ARMA Model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011
, 32(8)
: 1506
-1511
.
DOI: CNKI:11-1929/V.20110324.1201.008
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