对比经典模型与深度学习在发动机剩余寿命预测中的表现
Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches
- 用相同预处理流程比较回归、CNN和LSTM三种方法
- LSTM在两个数据集上分别达14.93和14.20的RMSE，优于以往复杂模型
- XGBoost在FD003上表现最佳，仅13.36的误差，适合工程实用
剩余使用寿命（RUL）估计是预测与健康管理（PHM）的关键环节，有助于实现设备的主动维护并减少非计划停机。本文基于NASA C-MAPSS涡轮风扇发动机数据集，对多种机器学习方法进行了对比研究：经典基线模型（岭回归、多项式岭回归、XGBoost）、一维卷积神经网络（1D CNN）和长短期记忆网络（LSTM）。所有模型均在FD001和FD003子集上使用相同的预处理流程进行评估。在原始序列模型中，LSTM在FD001和FD003上的均方根误差（RMSE）分别为14.93和14.20，优于郑等（Zheng et al.）报道的深层LSTM（RMSE 16.14和16.18），尽管其采用更简单的单层结构。1D CNN在FD001和FD003上的RMSE分别为16.97和15.68，表现出在FD003上的竞争力，但在FD001上预测偏保守。岭回归在原始特征和工程特征上均有评估，其余经典模型仅使用工程特征。XGBoost在FD003上达到13.36的RMSE，凸显非线性建模的强大性能。
原文摘要 · Abstract (English)
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.
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