Short-term Prediction of Fault Characteristics of Marine Diesel Generator Set Based on SSA-Hurst-ARIMA Combinatorial Model
In order to improve the short-term prediction accuracy of fault characteristics of marine diesel generator sets,a combined prediction model based on Singular Spectrum Analysis(SSA),Hurst index and Auto-Regressive Integrated Moving Average(ARIMA)is established.Based on the operating data of a marine diesel generator set in an experiment,the data of supercharger lubricating oil pressure are selected to compare and analyze the forecasting effects of a single ARIMA model,SSA principal component-ARIMA combination model and SSA-Hurst-ARIMA combination model.The results show that the prediction effect of SSA-Hurst-ARIMA combined model is better than that of the single ARIMA model and SSA principal component-ARIMA combined model,which is more suitable for the short-term prediction of fault characteristics of marine diesel generator sets.
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