Aeroengine gas trajectory prediction using time-series analysis auto regressive integrated moving average

PurposeThis study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine. Design/methodology/approachThe study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines. FindingsThe proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate. Research limitations/implicationsTo determine whether or not the two series were stationary, the Augmented Dickey-Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function. Originality/valueFurther, the authors find that the trend of predicted values and original values are similar and the error between them is small.

Automated summary

This study proposes the use of time series ARIMA models to predict the gas path performance in aero engines. The models accurately predict the performance parameters, which can improve the safety and efficiency of the engines. The methodology can be used for real-time monitoring and controlling of the gas path performance.