This paper introduces an unsupervised health-monitoring framework for turbofan engines that does not require run-to-failure labels. First, operating-condition effects in NASA CMAPSS sensor streams are removed via regression-based normalisation; then a Long Short-Term Memory (LSTM) autoencoder is trained only on the healthy portion of each trajectory. Persistent reconstruction error, estimated using an adaptive data-driven threshold, triggers real-time alerts without hand-tuned rules. Benchmark results show high recall and low false-alarm rates across multiple operating regimes, demonstrating that the method can be deployed quickly, scale to diverse fleets, and serve as a complementary early-warning layer to Remaining Useful Life models.

翻译：本文提出一种无需运行至失效标签的涡扇发动机健康监测无监督框架。首先，通过基于回归的归一化方法消除NASA CMAPSS传感器数据中的运行工况影响；随后仅使用各运行轨迹的健康段训练长短期记忆（LSTM）自编码器。通过自适应数据驱动阈值估计的持续重构误差可触发实时警报，无需人工调整规则。基准测试结果表明，该方法在多种运行工况下均能实现高召回率与低误报率，证明其具备快速部署能力、可扩展至多样化机队，并可作为剩余使用寿命模型的补充性早期预警层。