Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders

• URL: http://arxiv.org/abs/2601.10269v1
• Date: Thu, 15 Jan 2026 10:38:14 GMT
• Title: Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders
• Authors: P. Sánchez, K. Reyes, B. Radu, E. Fernández,
• Abstract summary: This paper introduces an unsupervised health-monitoring framework for turbofan engines that does not require run-to-failure labels.<n>First, operating-condition effects in NASA CMAPSS sensor streams are removed via regression-based normalisation.<n>Long Short-Term Memory (LSTM) autoencoder is trained only on the healthy portion of each trajectory.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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