Boosting Regression Assistive Predictive Maintenance of the Aircraft Engine with Random-Sampling Based Class Balancing

This study presents the development of a data-driven predictive maintenance model in the context of
industry 4.0. The solution is based on a novel hybridization of Remaining Useful Life (RUL) gener-
ation, Min-Max normalization, random-sampling based class balancing, and XGBoost regressor. The
applicability is tested using the NASA’s C-MAPSS dataset, which contains aircraft engine simulation
data. The objective is to develop an effective and Artificial Intelligence (AI) assistive automated aircraft
engine’s RUL predictor. It can maximize the benefits of predictive maintenance. The rules based RUL
generation provides a ground truth for evaluating the performance of intended regressors. The Min-Max
normalization linearly transforms the intended dataset and scales the multi subject’s data in a common
range. The imbalance presentation among intended classes can lead towards a biasness in findings. This
issue is intelligently resolved using the uniformly distributed random sub-sampling. Onward, the perfor-
mance of robust machine learning and ensemble learning algorithms is compared for predicting the RUL
of the considered aircraft engine by processing the balanced dataset. The results have shown that the
XGBoost regressor, uses an ensemble of decision trees, outperforms other considered models. The root
mean square error (RMSE) and mean absolute error (MAE) indicators will be used to evaluate the pre-
diction performances. The devised method secures the RMSE value of 12.88%. It confirms a similar or
better performance compared to the state-of-the-art counterparts.