Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors...

Descripción completa

Detalles Bibliográficos
Autores principales: Rombach, Katharina, Michau, Gabriel, Fink, Olga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161334/
https://www.ncbi.nlm.nih.gov/pubmed/34065164
http://dx.doi.org/10.3390/s21103550

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8161334/
https://www.ncbi.nlm.nih.gov/pubmed/34065164
http://dx.doi.org/10.3390/s21103550

Ejemplares similares