Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders
This provides a practical solution for predictive maintenance in aviation by enabling quick deployment and scalability to diverse fleets, though it is incremental as it builds on existing autoencoder and normalization techniques.
The paper tackles early fault detection in turbofan engines by developing an unsupervised health-monitoring framework using LSTM autoencoders, achieving high recall and low false-alarm rates across multiple operating regimes without requiring run-to-failure labels.
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.