This study extends previous research on Remaining Useful Life (RUL) prediction for agricultural vehicles by utilizing an enriched dataset to overcome earlier limitations in forecasting RUL for electric and hydraulic system components. Influential features have been identified through Pearson correlation and Random Forest feature importance analysis. Discrete Wavelet Transform (DWT) has been applied to extract additional approximation and detail coefficients, enhancing the feature set. Prediction algorithms—LSTM, FCNN, and SVM—have been evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²) metrics. Results indicate that LSTM models demonstrate superior performance, particularly those incorporating DWT- extracted features and geospatial factors such as weather and terrain conditions. The findings suggest that the developed RUL prediction models can be integrated into future Internet of Things (IoT) systems for remote monitoring and predictive maintenance of agricultural machinery.
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