Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder

Most Remaining Useful Life (RUL) prognostics are obtained using supervised learning models trained with many labelled data samples (i.e., the true RUL is known). In aviation, however, aircraft systems are often preventively replaced before failure. There are thus very few labelled data samples avail...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 117; S. 105582
Hauptverfasser:	de Pater, Ingeborg, Mitici, Mihaela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2023
Schlagworte:	Attention Autoencoder Health indicators Remaining Useful Life prognostics Unlabelled data samples Varying operating conditions Varying operating conditions Remaining Useful Life prognostics Autoencoder Unlabelled data samples Health indicators Attention
ISSN:	0952-1976, 1873-6769
Online-Zugang:	Volltext
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