通过分段建模提升发动机剩余寿命预测的不确定性表征能力。
Bifurcated Remaining Useful Life Prediction: A Hybrid Approach for Realistic Uncertainty Characterization
- 将运行状态分为健康与退化两阶段，分别采用不同模型建模。
- 在寿命末期预测置信度高，早期运行波动也能准确反映不确定性。
- 适合需要风险可控维护决策的工业场景，如航空发动机运维。
本研究提出一种新型混合预测框架，用于基于NASA C-MAPSS数据集的涡轮风扇发动机剩余使用寿命（RUL）不确定性感知估计。该框架采用状态感知策略，将发动机运行寿命划分为“健康”和“退化”两个阶段。基于LSTM的自编码器仅使用正常数据（RUL > 150周期）训练，通过重构误差实现鲁棒的状态分类。健康阶段采用条件威布尔生存分析进行均值残余寿命估计；退化阶段则使用带蒙特卡洛丢弃的概率神经网络，同时捕捉认知不确定性和随机不确定性。不采用刚性二值标签，而是通过校准的sigmoid函数将自编码器输出转换为连续状态概率，动态加权最终集成预测。该框架优势在于生成物理一致的不确定性区间，在接近寿命终点时提供高置信度预测，同时准确反映早期运行阶段的固有波动，为风险驱动型维护提供可靠工具。
原文摘要 · Abstract (English)
This study presents a novel hybrid prognostic framework for uncertainty-aware Remaining Useful Life (RUL) estimation in turbofan engines using the NASA C-MAPSS dataset. The framework employs a state-aware strategy that bifurcates the engines operational lifespan into "healthy" and "degraded" regimes. An LSTM-based autoencoder, trained strictly on nominal data (RUL > 150 cycles), monitors reconstruction error to act as a robust state classifier. For the healthy regime, a Conditional Weibull Survival Analysis is used for Mean Residual Life estimation. For the degraded regime, a Probabilistic Neural Network with Monte Carlo Dropout captures both aleatoric and epistemic uncertainties. Rather than using rigid binary labels, a calibrated sigmoid function converts the autoencoders output into continuous state probabilities, dynamically weighting the final ensemble prediction. The primary strength of this framework is its generation of physically consistent uncertainty bands, yielding high-confidence predictions near end-of-life while accurately reflecting the inherent variance of early operation, providing a robust tool for risk-informed maintenance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。