用无监督LSTM自动编码器实现发动机早期故障实时预警。
Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders
- 通过回归归一化去除工况影响，再用健康数据训练LSTM自编码器。
- 重建误差持续超标即触发告警，召回率高且误报率低。
- 无需故障标签，适合快速部署于多类型发动机群。
本文提出一种无需运行至失效标签的涡扇发动机无监督健康监测框架。首先通过基于回归的归一化方法消除NASA CMAPSS传感器流中的工况影响；随后仅在每条轨迹的健康阶段训练长短期记忆（LSTM）自编码器。利用自适应数据驱动阈值估计持续的重建误差，实现无需人工调参的实时告警。基准测试显示，该方法在多个工况下均表现出高召回率和低误报率，证明其可快速部署、适配多样机队，并作为剩余使用寿命模型的互补早期预警层。
原文摘要 · Abstract (English)
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.
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