Cargando…
Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes
In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In t...
Autores principales: | Ahn, Jisu, Lee, Younjeong, Kim, Namji, Park, Chanho, Jeong, Jongpil |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490086/ https://www.ncbi.nlm.nih.gov/pubmed/37687787 http://dx.doi.org/10.3390/s23177331 |
Ejemplares similares
-
Enhancing anomaly detection in distributed power systems using autoencoder-based federated learning
por: Kea, Kimleang, et al.
Publicado: (2023) -
Manufacturing Process Anomaly Detection in RF Cavities
por: Agius, Ryan
Publicado: (2021) -
Developing an Improved Ensemble Learning Approach for Predictive Maintenance in the Textile Manufacturing Process
por: Hung, Yu-Hsin
Publicado: (2022) -
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets
por: Abdallah, Mustafa, et al.
Publicado: (2023) -
Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance
por: Bemani, Ali, et al.
Publicado: (2022)