A Support Vector Regression Approach for Predicting the Remaining Useful Life of Turbofan Engines

Muhammad Vio Hardiansyah, Fitri Insani (Scopus ID: 57190404820), Lestari Handayani, Jasril Jasril, Suwanto Sanjaya

Abstract


Turbofan engines are crucial components in the aviation and manufacturing industries, where estimating the Remaining Useful Life (RUL) has a significant impact on operational efficiency and safety. This study aims to predict the RUL of turbofan engines using the Support Vector Regression (SVR) method, a machine learning approach that has proven effective in modeling nonlinear relationships between variables. Operational data related to turbofan engines include operational parameters, sensors, and maintenance records. The initial stage of this research involves data analysis based on unit number, time, operational control, and sensor parameters. This process begins with preprocessing to initialize the initial data values, normalize, and select sensors that have stagnant values, as these sensors do not affect the machine learning system. Subsequently, regression calculations are performed to compare predicted values and actual values using the Support Vector Regression method optimized with Grid Search Optimization. In this study, testing was conducted with Parameters C [1, 10, 50, 100] and ε [1, 5, 10, 50], resulting in the best model with an RMSE error of 19.56 and MAE of 14.73.


Keywords


Grid Search Optimization, Prediction, RUL, SVR, Turbofan.

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DOI: http://dx.doi.org/10.24014/coreit.v11i2.38532

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