An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries

Accurate and timely detection of anomalies in lithium-ion batteries is crucial for ensuring their reliability and safety. Complex degradation patterns and limited availability of labeled data pose significant challenges in identifying abnormal behaviors in battery usage. This paper proposes an unsup...

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Veröffentlicht in:	Reliability engineering & system safety Jg. 259; S. 110926
Hauptverfasser:	Chae, Sun Geu, Bae, Suk Joo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2025
Schlagworte:	Deep autoencoder Latent features Reliability test Unsupervised clustering Virtual health indicator Latent features Reliability test Deep autoencoder Unsupervised clustering Virtual health indicator
ISSN:	0951-8320
Online-Zugang:	Volltext
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