dorsal/arxiv
View SchemaEarly Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders
| Authors | P. Sánchez, K. Reyes, B. Radu, E. Fernández |
|---|---|
| Categories | |
| ArXiv ID | 2601.10269vv1 |
| URL | https://arxiv.org/abs/2601.10269 |
| 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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"date_created": "2026-02-17T05:53:24.279000Z",
"date_modified": "2026-02-17T05:53:24.279000Z",
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"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.",
"arxiv_id": "2601.10269",
"authors": [
"P. S\u00e1nchez",
"K. Reyes",
"B. Radu",
"E. Fern\u00e1ndez"
],
"categories": [
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Early Fault Detection on CMAPSS with Unsupervised LSTM Autoencoders",
"url": "https://arxiv.org/abs/2601.10269",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "6962011a-4294-4a85-9a62-2f984bc39842",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
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