Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model...

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Detalles Bibliográficos
Autores principales: Lee, Jeong-Geun, Kim, Deok-Hwan, Lee, Jang Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649564/
https://www.ncbi.nlm.nih.gov/pubmed/37960388
http://dx.doi.org/10.3390/s23218688

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649564/
https://www.ncbi.nlm.nih.gov/pubmed/37960388
http://dx.doi.org/10.3390/s23218688

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