Engine Failure Prediction on Large-Scale CMAPSS Data Using Hybrid Feature Selection and Imbalance-Aware Learning

Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness. This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail. It uses the NASA CMAPSS dataset, which has over 200,000 engine cycles from 260 engines. The process begins with systematic preprocessing, which includes imputation, outlier removal, scaling, and labelling of the remaining useful life. Dimensionality is reduced using a hybrid selection method that combines variance filtering, recursive elimination, and gradient-boosted importance scores, yielding a stable set of 10 informative sensors. To mitigate class imbalance, minority cases are oversampled, and class-weighted losses are applied during training. Benchmarking is carried out with logistic regression, gradient boosting, and a recurrent design that integrates gated recurrent units with long short-term memory networks. The Long Short-Term Memory–Gated Recurrent Unit (LSTM–GRU) hybrid achieved the strongest performance with an F1 score of 0.92, precision of 0.93, recall of 0.91, Receiver Operating Characteristic–Area Under the Curve (ROC-AUC) of 0.97, and minority recall of 0.75. Interpretability testing using permutation importance and Shapley values indicates that sensors 13, 15, and 11 are the most important indicators of engine wear. The proposed system combines imbalance handling, feature reduction, and Interpretability into a practical design suitable for real industrial settings.