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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19000557 |
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Table of Contents:
- <p>Unplanned industrial equipment failures cause significant financial losses and<br>safety hazards across aviation, energy, and manufacturing sectors. Predictive Maintenance<br>(PdM) addresses this by estimating the Remaining Useful Life (RUL) — the number<br>of operational cycles before a machine requires intervention — enabling proactive, costoptimal scheduling. This paper presents a complete end-to-end machine learning pipeline<br>for RUL prediction on the NASA C-MAPSS turbofan engine degradation dataset [17]. We<br>compare four models — Linear Regression, Random Forest, Gradient Boosting, and a twolayer Long Short-Term Memory (LSTM) network implemented in pure NumPy under<br>rigorous Group K-Fold cross-validation to eliminate engine-level data leakage. Our feature<br>engineering pipeline transforms 21 raw sensor channels into 37 temporal features including<br>rolling statistics, a composite health index, and degradation rate. Random Forest<br>achieves the best performance: RMSE = 9.95 cycles, R2 = 0.9398, MAE = 5.77,<br>and NASA PHM asymmetric score = 1.9. We further contribute bootstrap prediction<br>intervals achieving 90.0% empirical coverage at the 95% nominal level, and a maintenance<br>scheduling module projecting $430,000 in cost savings on a 20-engine fleet. </p>