Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches

TL;DR

This study compares classical, CNN, and LSTM machine learning models for estimating the remaining useful life of turbofan engines using the NASA C-MAPSS dataset, highlighting the strengths of LSTM and XGBoost.

Contribution

It provides a fair comparison of different RUL estimation approaches on the same dataset and preprocessing pipeline, revealing the effectiveness of LSTM and XGBoost.

Findings

LSTM outperforms previous deep LSTM models with RMSE of 14.93 and 14.20.

XGBoost achieves RMSE of 13.36, showing strong nonlinear modeling performance.

Classical models like Ridge Regression and CNN also demonstrate competitive results.

Abstract

Remaining Useful Life (RUL) estimation is a critical component of Prognostics and Health Management (PHM), enabling proactive maintenance scheduling and reducing unplanned failures in industrial equipment. This paper presents a comparative study of machine learning approaches for RUL estimation on the NASA C-MAPSS turbofan engine dataset: classical baselines (Ridge Regression, Polynomial Ridge, and XGBoost), a 1D Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM) network. All models are evaluated on the FD001 and FD003 subsets under an identical preprocessing pipeline to ensure a fair comparison. Among raw-sequence models, the LSTM achieves RMSE of 14.93 and 14.20 on FD001 and FD003 respectively, outperforming the deep LSTM reported by Zheng et al.~\cite{paper} (RMSE 16.14 and 16.18) despite using a simpler single-layer architecture. The 1D CNN achieves RMSE of 16.97 on FD001 and 15.68 on FD003, demonstrating competitive performance on FD003 while producing more conservative RUL predictions on FD001. Ridge Regression is evaluated on raw and engineered features, while other classical models use only engineered inputs. XGBoost achieves an RMSE of 13.36 on FD003, highlighting the competitiveness of nonlinear modeling.